Oportunidades Públicas

Increased evaporation due to advection of dry air

Investigating the "oasis effect" which can increase the evaporation rates in bodies of water that are located in areas hyper-arid.

Evidence-based Policy and Innovation Research Lab

Contact professor

The green tax on vehicles. Does it work?

Every new car that is sold in Chile must pay a tax to the “green” that depends on their emissions of NOx and CO2 (the exact amount for each vehicle can be calculated in https://www4.sii.cl/calcImpVehiculoNuevoInternet/internet.html ) . Although the origin of this tax is the levy (it was part of the Tax Reform of 2014 to fund free university), can have a positive environmental impact. This research aims to estimate the effect of this tax on the emissions of the new car fleet. What has changed the average emission of the fleet? That is to say do You sell cars more clean today than yesterday? The brands are ahead of the compliance with future standards for their models to pay less tax?
All of these questions are very important to assess the impact of the tax, and to assess its performance as an instrument of environmental regulation.

Quantification of remodeling in patients with tetralogy of Fallot

1 in 2000 kids are born with tetralogy of Fallot, a combination of 4 serious congenital heart defects. To overcome these problems, the patient undergoes a series of surgeries and the heart responds by remodeling, adapting its form and function. However, the way this adaptation occurs is not fully characterized. The goal of this study is to locally quantify the remodeling of the heart in response to these surgeries. We will magnetic resonance images and the finite element method. This research will enable the development of predictive models to help the planning and optimization of the current surgeries.

Size and scale of volcanoes in different environments of the cortex

This project will use data from a database data of morphometry of volcanoes (i.e., Grosse et al., 2008) to understand the distribution of the size and scale of the volcanoes. The student will perform statistical analysis and thermodynamic

High-order boundary integral equation methods for Stokes flow problems

I invite applications of highly motivated undergraduate students to pursue research on novel boundary integral equation methods for the solution of Stokes flow problems with applications in biology. Successful candidates are expected to have basic knowledge in the following areas: fluid mechanics, scientific computing, and programming in Matlab, Julia, or Python.

Biodesign: Research in engineering design for health

Biomass transformation

By means of using MOF-type catalysts, biomass will be transformed into value-added products, for which various chemical processes will be carried out, in which the obtained products can be characterized.

Learning from data: using real-time data to create context and support decisions in offsite construction

Offsite construction is a disruptive strategy where buildings are manufactured in factories and shipped to its destination at a superior quality and shorter schedules. Current design and manufacturing phases generates a significant volume of data from different sources that, if properly combined, presents a great opportunity to identify meaningful insights and improvements for future operations . Hence, this research proposes the application of visualization techniques and the generation of key performance indicators (KPI) to identify production patterns such as workstation’s utilization, queue length, etc. Furthermore, these kpi’s will further address the effect of specific project attributes during the manufacturing process. An overall assessment is performed, and suggestions are made for future applications in offsite construction. This research will utilize an offsite construction facility in Alberta, Canada as case study where large sets of data containing the durations and characteristics of the manufactured panels will be provided.

Robotics Mechanism Design

design of robotic mechanisms using of electromechanic, pneumatic, or hydraulic actuators, with computer motion control. Examples are robotic arms, autonomous vehicles, intelligent prostheses, energy systems, etc. These mechanisms will be fabricated by technologies such as 3D Printing, CNC, and fiberglass reinforced plastics

Computational creativity in generative networks for audio

Design and Fabrication of a Three-Motor Dicing System for Precision Cutting of Silicon Wafers and Electronic Circuits

The project proposal seeks to design and implement a dicing system, intended for the precise cutting of silicon parts and electronic circuits. The project will incorporate three independent motors to ensure greater precision and versatility in cuts. Key aspects will be addressed such as process automation, efficiency in the handling of delicate materials and the integration of advanced technologies to improve the quality of electronic devices manufactured in the UC clean room. This project will provide students with valuable hands-on experience in the application of manufacturing techniques from the semiconductor industry.

Memory and knowledge: mapping civic knowledge to improve hydrological and hydrogeological knowledge

Compile the community's knowledge about the hydrological state and its evolution over time (perennial and intermittent rivers, water outcrops).
Map this knowledge in GIS tools (QGIS, Google Earth)
Site study: Parque Nacional del Río Clarillo (RM)

Bioremediation

Development of biological systems for the treatment of contaminants.

Fragility Curves and Mitigation Strategies for Timber Structures under Earthquakes and Tsunamis

This project aims to develop fragility curves for timber structures built either formally or informally, considering multiple hazards such as earthquakes and tsunamis. It will also evaluate specific mitigation measures for each hazard, analyzing potential synergies and conflicts between them. Desired: experience in numerical modeling and knowledge of construction systems. The student will learn how to develop fragility curves (probability of damage vs. hazard intensity), perform linear and nonlinear structural modeling, and formulate strategies to optimize the performance of these structures under multi-hazard scenarios.

CO2 Emissions quotas in the Climate Change Framework Law

La Ley 21455 - Ley Marco de Camblio Climatico - Art. 5(b) define “ Presupuestos sectoriales de emisiones de gases de efecto invernadero al año 2030 asignados a los sectores señalados en el artículo 8°, de acuerdo a criterios de costo efectividad y equidad.” Esto se logra a traves de normas de emision para cada sector.
Esta investigacion indaga en como aterrizar estos conceptos considerando los dos criterios, y cuanto pueden cambiar los presupuestos segun qué criterio se considere.

Asignar los presupuestos es vital para que cada sector defina sus esfuerzos de mitigacion. El resultado de la investigacion puede ayudar a la politica publica. Ideal que hayan cursado ICS3742. (esta oportunidad se ofrece en 5 o 10 créditos)

Electrification and Restoration Study of the UC East Campus Organ

The UC Campus Oriente organ, a historic instrument whose original pneumatic action has been completely destroyed, poses a technical and heritage challenge. This project seeks to conduct a preliminary feasibility study for its electrification, exploring the use of solenoids to open the secret valves that allow air to pass through, replacing the damaged original mechanisms. The study will include a technical analysis of possible solutions and the conceptual design of the electrical system, with a view to restoring its functionality and opening new possibilities for technological and musical experimentation. This research represents a unique opportunity to integrate electrical engineering knowledge with the preservation and modernization of an emblematic musical instrument. Professors: Rodrigo Cádiz (Engineering and Music), Patricio de la Cuadra (Engineering and Music), José Manuel Izquierdo (Music).

Advanced Numerical Modeling and Materials Analysis

Are you ready to tackle cutting-edge challenges in computational engineering and materials science? Join our dynamic research team to develop innovative techniques that combine voxel-based finite element methods (V-FEM) with powerful computational tools, advancing applications in medical engineering, materials science, and mechanical systems.

Voxel-based finite element methods leverage high-resolution image data to create precise models of complex structures, enabling unprecedented accuracy in simulations. These approaches find critical applications in medical fields, such as bone mechanics and implant design, and in materials science, where understanding microstructural behavior is essential for innovation.

Optimizing Bacterial Self-Healing in Concrete: Encapsulation vs. Direct Addition for Enhanced Performance

This research explores the innovative autonomous self-healing approach in concrete, which incorporates bacteria to enable self-repair of cracks. The focus is on evaluating two primary methods: direct addition of bacteria to the concrete mixture and encapsulation of bacteria within microcapsules.

In the direct addition method, bacteria and nutrients such as calcium lactate are embedded into the concrete. When cracks form and water seeps in, the bacteria activate and convert nutrients into calcium carbonate (CaCO₃), effectively sealing the cracks. The encapsulation method, on the other hand, uses microcapsules containing bacteria, which rupture upon crack formation to release healing agents that repair the damage.

This research explores the innovative use of bacteria for autonomous self-healing in concrete. It evaluates two main methods: direct addition of bacteria to the concrete mix and encapsulation in microcapsules.

Direct addition activates bacteria upon water ingress to produce calcium carbonate (CaCO₃) and seal cracks, while encapsulation releases repair agents when microcaps

Monitoring Seismicity in Front of the Juan Fernández Ridge

Referencias:
• Nesterov, Y., Spokoiny, V. Random Gradient-Free Minimization of Convex Functions. Found Comput Math 17, 527–566 (2017)
• Diakonikolas J., Guzmán C.: Optimization on a Finer Scale: Bounded Local Subgradient Variation Perspective. ArXiv:2403.16317 (2024)

Exploring environmental data for observing salt intrusion in rivers and estuaries

B+tree index compression - a case study

We will study index compression schemes for B+tree storage supported in popular relational and graph engines. In particular, the project consists of testing schemes that were already implemented in the MillenniumDB graph engine and showing what is their effect on query performance and data storage. We will do an experimental suite on the Wikidata knowledge graph which comprises multiple TB of data, underlying the importance of good compression scheme.

Are columnstores wortless when it comes to knowledge graph data management?

He we test how well modern columns store systems perform when managing real world knowledge graph query/data load. The main objective of the project will be to set up multiple columnar systems and tune them sufficiently enough to be able to handle the reduced version of the Wikidata knowledge graph. We will then test their performance on a realistic query load consisting of user posted Wikidata queries.

Transaction processing in IIC3413DB

In this project we will add basic support for several transaction processing pipelines to IIC3413DB which is used in our database course. The project will start by implementing basic MVCC mechanism supporting the READ COMMITED isolation level and progress to Snapshot Isolation. Finally, we will add support for SERIALIZABLE isolation level using simple in-memory metadata mechanisms. Knowledge of C++ is required for this project.

Modelling water transport in porous soils

Part of Carlos Faúndez Fondecyt de Iniciación Proyect: Field-scale estimation of macropore parameters for dual permeability models: An integrative approach incorporating pore-scale modeling and applied geophysics.

1. Implement a unsteady 1-D model for the transport of volumetric water content and hydraulic head in a heterogeneous soil column.

2. Analyse the effect of the non-linearity induced by the dependence of the soil hydraulic conductivity on the quantities transported.

3. Extend 1-D model to a 2-D unsteady model with azimuthal symmetry.

* The development of the model will be in Python on Julia. However, the final version of this model must translated to R. The co-supervisor Carlos Faúndez from Ing. Hidráulica y Ambiental will guide and support the translation of the code.

Generative network of musical exercises for auditory training

In this research, we will attempt to train a generative deep learning model based on a database of music theory and auditory training exercises, using multimodal models that receive text in addition to scores. The idea is to implement a tool for personal study without the need for a teacher as a mediator.

Automatic detection of audio signatures in underwater acoustic scenes

Marine ecosystems can be studied through recordings of underwater acoustic scenes using hydrophones. The objective of this project is to develop and to implement efficient methods for the detection of acoustic signatures of distinct fish species living in these ecosystems. This problem is part of an interdisciplinary collaboration with marine biologists which poses interesting computational challenges. This is due to the length of the audio recordings, the reduced number of acoustic signatures, and the low signal to noise ratio due to the diversity of the acoustic scene.

From a mathematical perspective, the detection is formulated similarly to blind deconvolution. The signal is modeled as a superposition of a few components modulated in frequency and corrupted by additive noise. To identify these components, we avoid a black-box approach, solving instead a semidefinite program. The computational challenge involves developing efficient solvers able to scale up to long audio recordings with low signal to noise ratio.

Digital Health – Software Quality

The advancement of digital health has led to a proliferation of technological solutions—such as telemedicine platforms, mobile applications, electronic health record systems, and applied artificial intelligence—that do not always comply with the required standards to ensure safety, interoperability, usability, and clinical effectiveness. This situation poses risks to patients, creates challenges in integrating with existing systems, and limits the ability of healthcare providers to assess the true quality of the tools they use or acquire. At the national level, this project proposes to explore and develop a comprehensive software quality assurance model specifically focused on the field of digital health, adapted to the regulatory, technical, and clinical conditions of the Chilean and Latin American context.

The model will include evaluation criteria based on international best practices (such as ISO/IEC 25010), automated testing tools, developer guidelines, and a quality seal to identify safe and reliable solutions.
This project will not only promote the continuous improvement of the digital health innovation ecosystem but also support informed decision-making by providers, patients, and public institutions.

Study of the Shroud of Turin by image processing

The image on the Shroud remains a subject of study and controversy to this day. It is proposed to systematically investigate the image (positive and negative) of this relic using various image processing and analysis techniques based in artificial intelligence to compare it against similar historical images and find correlation patterns among these pictorial sources. As a result, it is hoped that a paper will be written for an international conference on this subject.

Anaerobic systems for the treatment of emerging contaminants

This is an applied research project.
The student will work on the development and application of laboratory-scale anaerobic and bioelectrochemical reactors to remove emerging contaminants present in domestic and industrial wastewaters.
The student will learn advanced laboratory techniques for wastewater characterization. They will learn how to feed and operate anaerobic and bioelectrochemical reactors, as well as how to monitor and evaluate their performance based on effluent quality and the removal of specific contaminants.
If you are interested, please contact me at rolabatut@uc.cl to schedule a meeting to discuss details and answer any questions you may have before making a decision.

Gamification and Mixed Reality in Construction: Review and Research Opportunities

This project focuses on reviewing and analyzing the existing literature on the application of gamification and extended reality technologies (with an emphasis on mixed reality) in the training of construction workers, particularly in the field of ergonomics and the prevention of work-related musculoskeletal disorders.

The study aims to identify:
- The main trends and theoretical approaches in gamification applied to safety and ergonomics education.
- The current state of mixed reality use in construction training environments.
- The limitations and research gaps in integrating these technologies to foster ergonomic behavior change in real workplace contexts.

This study is conceived as an initial step to support future, larger-scale research aimed at developing practical immersive training platforms. At this stage, however, it will be limited to a systematic bibliographic study, with emphasis on recent academic publications.

Bioinformatics and modeling of the gut-brain axis

Comparative genomics of probiotic bacteria, literature review, and retrieval of metabolic reconstructions from databases. Analysis of transcriptomic and metagenomic microbiome databases. Reconstruction of metabolic models integrated with transcriptomic data.
Development of metabolic reconstructions in Drosophila melanogaster for studies on neurodegeneration and the gut–brain axis.

Using generative AI for the de novo design of the metamorphic domain of RfaH in its autoinhibited state

The metamorphic protein RfaH is a bacterial virulence factor, which has two domains: NTD and CTD. The CTD reversible interconverts from an alpha-helical into a beta-barrel state through a metamorphic process. Although the possible folding routes between both states have been studied, it is unknown if there are other alternatives of folding topologies for the CTD in the autoinhibited state. In this regard, the use of generative AI tools for de novo protein structure design, such as RFDiffusion, allow to explore novel structures never seen in nature. The purpose of this research is, therefore, to ascertain if RFDiffusion allows to generate novel structures for the CTD of RfaH in the autoinhibited state, as well as analyzing the diversity of generated structures via clustering methods based on structural superposition metrics.

Building a Multi-Cell Migration Model with Intercellular Interactions

This project aims to develop a new multi-cell computational model from scratch, incorporating intercellular interactions, geometric constraints, and emergent collective dynamics. The conceptual basis draws from https://doi.org/10.1073/pnas.1521151113 and https://doi.org/10.1101/2025.08.28.672905.
The student will design agent-based rules and implement efficient, object-oriented code capable of handling cell–cell forces, boundary constraints, interaction radii, and events such as extrusion or collision avoidance.
Best suited for students with a computational engineering or software background. Biological knowledge is not required; computational rigor, independence, and motivation to contribute to cancer research are essential.

Evaluation of Multifrequency Deep Image Prior for Real-Time 0.55 T Radial Cardiac Cine MRI

Cardiac cine Magnetic Resonance Imaging (MRI) is the gold standard for assessing heart function, but conventional techniques rely on electrocardiogram(ECG)-based acquisitions over multiple heartbeats, which limits temporal resolution and excludes patients with irregular heart rhythms. Recent advances in artificial intelligence have introduced Time-Dependent Deep Image Prior (TD-DIP), a powerful method that enables high–temporal-resolution cardiac imaging from very short MRI acquisitions. However, standard TD-DIP still depends on ECG signals, restricting its applicability in patients with arrhythmias. The Multifrequency TD-DIP framework overcomes this limitation by learning cardiac motion directly from the data—without requiring ECG input. This method has shown promising results at 1.5 T using spiral acquisitions. The objective of this project is to implement and evaluate Multifrequency TD-DIP for radial cardiac cine MRI at 0.55 T. Low-field MRI systems offer improved patient comfort, lower costs, and greater accessibility, but they also present exciting challenges due to reduced signal-to-noise ratio (SNR). This project offers hands-on experience at the intersection of MRI physics, signal processing, and deep learning, contributing to more accessible and patient-friendly cardiac MR imaging technologies.

Universal Discontinuity Index–Driven Digital Twins for Multiscale Damage and Stability in Brittle Rock Masses

This IPre project is embedded within a broad research framework focused on the development of Digital Twin–based approaches for rock mass behavior in mining and underground engineering. The research aims to advance the understanding of how geometric discontinuities, stress conditions, and scale effects jointly control damage evolution, anisotropy, and structural stability in brittle rock masses.
A central element of this framework is the Universal Discontinuity Index (UDi), which serves as a unifying structural descriptor to quantify fracture activation, mechanical degradation, and failure mechanisms across a wide range of mining-related problems. By integrating UDi into Digital Twin models, the research establishes a consistent link between rock mass geometry, loading conditions, and mechanical response, enabling systematic interpretation across different engineering contexts.
The Digital Twin framework developed in this project is applicable to multiple domains of mining engineering, including:
• uniaxial and confined rock strength (UCS and biaxial conditions),
• pillar stability and strength evolution,
• support and reinforcement design in jointed rock masses,
• anisotropic fracture behavior associated with oriented discontinuities,
• stability assessment of underground excavations at different scales.
Within this approach, numerical and experimental models are treated as digital representations of physical rock systems, where damage indicators such as fracture intensity, activation mode, kinematic response, and UDi evolve together with stress and deformation. This enables a unified interpretation of failure processes, independent of the specific mining application.

Low-Alkali Cement-Free Tailings Binders

The construction industry is under increasing pressure to reduce cement consumption and its environmental footprint. One promising approach is the development of cement-free binders based on low-calcium (low-Ca) materials, where industrial by-products such as mine tailings are reused as alternative precursors for construction materials.
My current research focuses on alkali-activated materials (AAMs) produced from low-Ca tailings, with the aim of developing sustainable binders suitable for civil engineering applications. A key challenge in AAM technology is the reliance on high concentrations of alkali activators, particularly sodium hydroxide (NaOH), which raises concerns related to cost, safety, and environmental impact. This project specifically targets minimizing the use of alkali activators, while still achieving reliable and practical mechanical performance.
The research is mainly experimental and involves laboratory mixing, mechanical testing, and basic material characterization to evaluate the performance of tailing-based binders under reduced alkali activation. The broader goal is to move toward more practical, safer, and environmentally responsible alkali-activated systems for construction use.
I am currently seeking motivated students and early-career researchers with a background in civil engineering or related fields who are interested in sustainable construction materials and hands-on laboratory research. If you are excited about working on innovative, low-carbon binders and gaining practical research experience, I would be happy to collaborate.

Deep Learning–Based T1 and T2 3D Whole-Heart Mapping for Joint T1/T2 MRI

T1 and T2 mapping are cardiac Magnetic Resonance Imaging (MRI) techniques that have been shown to provide objective information about the myocardium, enabling the assessment of cardiac diseases that cannot be diagnosed using conventional cardiac imaging. Joint T1/T2 sequence enables the acquisition of simultaneous 3D T1 and T2 mapping from 3 water volumes. Since T1 and T2 maps are now estimated in 3D, this process can be time-consuming. The goal of this project is to train a neural network to directly estimate T1 and T2 maps from a novel joint T1/T2 sequence at 0.55T, reducing processing time and improving its accuracy.

Explorando la base de datos de vehículos autónomos de Waymo

Esta investigación de pregrado tiene como objetivo explorar el Waymo Open Motion Dataset para caracterizar el comportamiento de vehículos autónomos que operan servicios de transporte bajo demanda en seis ciudades de Estados Unidos. La hipótesis central del estudio es que los vehículos autónomos actualmente en operación presentan un comportamiento más conservador que los vehículos conducidos por humanos, lo que podría traducirse en una menor capacidad de la infraestructura vial.
Para evaluar esta hipótesis, se reconstruirán trayectorias vehiculares correspondientes tanto a vehículos autónomos como a vehículos conducidos por humanos. A partir de estas trayectorias, se identificarán segmentos de operación en condiciones cuasi estacionarias, los cuales permitirán la construcción de indicadores agregados o macroscópicos del comportamiento vehicular, tales como velocidad, densidad y flujo.
Las principales actividades del proyecto incluyen: (i) comprender la estructura y el contenido del Waymo Open Motion Dataset; (ii) extraer y procesar la información necesaria para la construcción de trayectorias vehiculares; (iii) aislar segmentos de estado estacionario a partir de dichas trayectorias; (iv) construir indicadores macroscópicos de desempeño; y (v) documentar y analizar los resultados obtenidos.

Identification of BIM roles and competencies

Survey of the main human resource roles in BIM (Building Information Modeling) processes and the competencies they need to perform them. The idea is to understand how different actors in the process, from technical labor to managerial roles, interact with BIM processes. The work requires reviewing literature and standards, as well as fieldwork through interviews with relevant companies. The deliverable is an article that both describes the performed work and proposes a matrix of roles with an associated set of BIM competencies.

Public Policy Monitor

Through the analysis of surveys and modern econometric statistics, students will work on the preparation of reports applied to labor economics, migration, and the control of criminal activity.

Although the grade will be individual, opportunities for pair or group work will be offered. Applied Econometrics (ICS2563) is a fundamental requirement, and priority will be given to those with Advanced Econometrics (ICS3564) or similar coursework in Database Management or Machine Learning. Please indicate your experience in one of these areas in your application.

Simulation of electrical conductivity in soils affected by desalination plant brine (reject water)

Desalination plants supply water with variable physico-chemical quality. In this project, we will focus on the electrical conductivity (EC) of the water—particularly brine (reject water) or blended mixtures—and its potential use in agriculture. The objective is to develop and/or apply a computational model to evaluate the evolution of soil EC (e.g., within the root zone) under different irrigation scenarios, and then determine which crops could grow under these conditions based on their salinity tolerance.
The work will be entirely computational; however, if the student wishes, a technical visit to a desalination plant in Chile’s Fifth Region could be arranged to provide context and to learn about the desalination process and the range of water qualities available.

Derivation of interfacial heat transfer coefficients for the non-isobaric evaporation of cryogenic liquids in storage tanks

Traditional models for cryogenic boil-off (such as 1-D evaporation models) often assume a well-mixed liquid phase or rely on static empirical correlations. However, in non-isobaric conditions, wall and bottom heating trigger natural convection plumes. These plumes disrupt thermal stratification and significantly enhance the cooling rate at the liquid-vapour interface.

The legitimate research gap lies in the decomposition of these heat transfer mechanisms. By using Direct Numerical Simulation (DNS) at a user-level (OpenFOAM), this project aims to characterize the "thermal skin" of the liquid to improve the fidelity of the Huerta and Vesovic (2025) model.

Impact:
- Optimising the design & operation of cryogenic storage tanks relevant to liquid hydrogen, liquid nitrogen, liquefied natural gas.
- Renewable energy storage

Construction progress control with augmented reality

The IPRE student will be supporting the research of a master's student. The tasks mainly include reviewing progress control technologies, testing an augmented reality tool for progress control, and assisting in experiments where augmented reality will be used to support progress control. Support in the experiment includes aiding in the execution of the experiment (giving instructions to participants, collecting data, etc.) as well as processing the results.

Reinforcement Learning for the Optimization of Cross-Training Workout Plans

In this IPRE, the student will develop Reinforcement Learning algorithms aimed at the optimization of personalized training plans for cross-training or CrossFit-style disciplines. The goal is to design training strategies that balance progression, intensity, and recovery, taking into account the athlete’s physiological adaptation and the prevention of overtraining.

As a first step, a dynamic model of the athlete will be developed, describing the evolution of variables such as fitness, fatigue, and performance as a function of training load. This model will serve as the basis for formulating the problem as a sequential control system, in which a reinforcement learning agent acts as an intelligent coach that selects training sessions over time.

The project will be approached from an intelligent control perspective, exploring both model-free and model-based methods, and validating the results through simulations.

Prediction of water repellency and moisture in forest soils using portable NIR spectroscopy

The student will propose a methodology to predict soil water repellency across increasing soil moisture levels, integrating laboratory analyses and predictive modeling based on NIR spectroscopy. The research will consider the influence of different soil moisture levels on water repellency and the NIR spectral response, together with a basic characterization of soil properties. As an outcome, predictive models will be developed using machine learning techniques to estimate both soil moisture and different indicators of water repellency.

Formulation of Quinoa-Based Cookies with Reduced Acrylamide and HMF Content Using Antioxidant Extracts

Primarily the Maillard reaction (MR) in food processing at high temperatures generates toxic compounds, such as acrylamide (AA) and hydroxymethylfurfural (HMF), which are neurotoxic and potentially carcinogenic. This has raised concerns about food safety and public health.To address these concerns, the development of gluten-free cookies based on quinoa, enriched with various antioxidant extracts rich in polyphenols, is proposed. This project aims to create a healthy and safe product aligned with the "clean label" trend and formulated for individuals with celiac disease. The antioxidant extracts help reduce the formation of toxic compounds such as AA and HMF, while maintaining sensory properties and increasing the antioxidant capacity of the cookies treated with the extracts.The effectiveness of the antioxidant extracts will be evaluated at different concentrations, analyzing their chemical safety. Ultimately, this research is expected to provide an innovative solution for the development of baked products, improving their safety and quality.

Development of an automated system for bioreactor monitoring

In this research, the student will develop a data acquisition system (DAQ) for the measurement and continuous monitoring of pH, temperature, and mainly the biogas produced by biological reactors used for water treatment and bioenergy production. The student is expected to be able to use Arduino, LabVIEW or similar, in addition to programming tools such as Python, R, or LabVIEW to develop the DAQ. Students will have at their disposal all the necessary materials (e.g. sensors, Arduino, LabVIEW, etc.) to achieve this objective.

Sensitivity and identifiability analysis of parameters in thermodynamic models

This research opportunity will study how thermodynamic model parameters influence the prediction of properties and phase equilibrium. For this task, simple models from the literature will be implemented and the sensitivity of their parameters will be analyzed using programming tools (Python and/or Julia) and numerical analysis. The expected result is the identification of the most influential parameters and the evaluation of possible identifiability problems in the estimation of parameters from experimental data.

Radiography of Process Mining: Uncovering the Secrets and Leaders of the ICPM Conference

[International Scientific Publication Opportunity]

How has Process Mining research evolved globally? In this project, you will explore the history and current state of the field's premier conference (ICPM) by extracting and analyzing DBLP data using Python. We will investigate shifts in thematic trends, discover collaboration patterns within the scientific community, and map the geography of industry leaders. The main objective is to transform this data into high-impact visualizations and co-author a scientific paper for international publication.

Expected profile and skills:
- Knowledge of Process Mining: Desirable, but NOT essential.
- Knowledge of Information Visualization: Desirable, but NOT essential.
- Strong motivation for data analysis using Python.

The DNA of Music: Decoding Sheet Music through Object-Centric Process Mining

[International Scientific Publication Opportunity]

Is it possible to analyze a symphony as if it were a process? While traditional Process Mining is limited to analyzing a single "case" at a time, music is inherently multidimensional: notes, measures, and instruments interact simultaneously. In this "out-of-the-box" project, we will apply Object-Centric Process Mining (OCPM) to structurally model sheet music and compositions. Our challenge will be to use Python to automatically convert musical formats into the OCEL (Object-Centric Event Log) standard and build an interactive dashboard to analyze these works. The final goal is to visually validate this conceptualization and co-author a paper for an international scientific publication.

Expected profile and skills:
- Knowledge of Process Mining: REQUIRED.
- Ability to read sheet music and musical theory: REQUIRED.
- Celonis Academy "Celonis Fundamentals Course": Desirable, but NOT essential.
- Celonis Academy "Build an Object-Centric Data Model" module: Desirable, but NOT essential.
- Motivation for data conversion and programming in Python.

Web Development to Humanize Massive Classes

Low participation and distractions are major challenges in massive courses. The literature shows that these problems could decrease when the teacher treats students as individuals and addresses them by their names. However, in large classes, memorizing all faces and names becomes a tedious and impractical task. To solve this, this research is based on FAIS, a system founded on memorization by repetition and Gamification techniques, which requires less effort by including a playful approach.

The objective of this project is to "humanize" the classroom by taking this solution to the next level: re-implementing the idea by developing a professional-standard, fully responsive web application (optimized for use on PCs and any mobile phone), with an impeccable user experience and a strong focus on Gamification. In addition to software development, the team will establish the methodological foundations for a scientific experiment to validate the tool in the future.

REQUIREMENTS:
- BEFORE APPLYING, read and understand: "FAIS: A System for Effectively Learning Students Names and Faces in Massive Courses" (http://ieeexplore.ieee.org.pucdechile.idm.oclc.org/document/9281182)
- Advanced knowledge in web application programming

VR-based design review

EVI Lab (Extended Reality Laboratory for Design and Construction) has developed a platform for collaboratively reviewing the design of projects (buildings, bridges, etc.) in virtual reality environments.
The objective of the IPRE is to carry out a case study in which this platform is used in an architecture and/or engineering firm.
The student will be responsible for designing the case study, training professionals to use the platform, measuring impact indicators, and writing a scientific article describing the findings.
The student will be supported by the development and research team at EVI Lab.

Transforming University Management: Digitalizing Processes in the Faculty of Education

This research opportunity focuses on the digitalization of management processes in higher education. The project involves a review of specialized literature on digitalization in university contexts, the analysis of both manual and digital processes carried out by the Research Office of the Faculty of Education, and the design of a concrete improvement proposal aimed at optimizing its management through digital solutions. This experience will allow students to develop competencies in applied research, process analysis, and digital transformation within real institutional contexts.

Estimation of Emission Reductions from Bus Electrification in Chile

Chile has committed to achieving carbon neutrality by 2050, a goal that requires significant transformations in the transport sector. Among its targets, the country has established that all new vehicles must be zero-emission by 2035, that the entire urban bus fleet must be zero-emission by 2040, and that the energy consumption of the transport system must be reduced by 25% by 2035, with one-third of that energy coming from renewable sources.

In this context, the electrification of urban public transport has advanced significantly through the renewal and tendering of electric bus fleets, supported by Law No. 20,378, associated with the National Public Passenger Transport Subsidy. Santiago currently has more than 4,000 electric buses, while 11 regions have already incorporated this technology.

The objective of this project is to quantify the environmental impact of incorporating electric buses into urban public transport in Chile by comparing the emissions avoided through the replacement of diesel buses with the additional emissions generated by the electricity demand required for their operation.

To this end, an hourly database will be developed, integrating bus activity levels by route and technology, fuel consumption of the diesel fleet and its associated greenhouse gas emission rates, charging profiles and electricity consumption of electric buses, electricity generation, and power system emissions. A central component of the study will be the estimation of hourly marginal emission factors, which will make it possible to identify the emissions attributable to electricity generation for bus charging with greater precision, rather than relying on average emission factors for the electricity mix.

Using this information, the study will estimate unit emissions per bus and aggregate emissions for the entire fleet under both the baseline and project scenarios, considering local pollutants and greenhouse gases. Finally, the study will quantify the social costs and benefits of electrification, including emission reductions, health benefits, and potential operational savings.

Foundations for Object-Centric Process Mining in Basketball

Data analysis in team sports is gaining popularity. However, traditional Process Mining approaches applied to sports are often limited to a single-case perspective, modeling only aspects such as team possession. This ignores crucial behaviors for understanding game performance, such as the actions of players who do not have the ball. Object-Centric Process Mining (OCPM) allows us to overcome this limitation by considering multiple objects that interact through their behavior, better reflecting the complexity of a match.

The objective of this project is to adapt a recent methodological framework—originally designed to transform object-centric football data with spatial context—and bring it into the basketball ecosystem. Through this approach, we seek to comprehensively model the interactions between various event classes (related to the game, the ball, and position) and multiple object types, such as the match, teams, players, possession, spatial positions on the court, and the ball itself.

In this stage of the research, the focus will not be on extracting or analyzing raw data, but on establishing the solid theoretical and methodological foundations for how to apply OCPM to basketball. This foundational design will serve as a critical pillar to, in the future, request and process databases from official entities (e.g., NBA). The team will work on conceptualizing this model to co-author a scientific article and position this framework within the sports analytics literature.

REQUIREMENTS:
- BEFORE APPLYING: Read and understand "Transforming Football Data into Object-Centric Event Logs with Spatial Context Information" by Vito Chan et al. 2026 (https://arxiv.org/pdf/2507.12504)
- BEFORE starting the IPRE, you must have completed the Celonis Fundamentals online course: https://academy.celonis.com/learning-paths/process-mining-fundamentals-for-students
- BEFORE starting the IPRE, you must have completed the Celonis OCPM online course: https://academy.celonis.com/learning-paths/implement-and-work-with-object-centric-process-mining
- Tactical knowledge of Basketball

Economic Assessment of Urban Runoff Impacts

The objective of this research project is to advance current approaches for evaluating the impacts of urban drainage practices. Specifically, it aims to develop a methodology for quantifying the savings and cost reductions associated with the implementation of storage and infiltration measures within drainage systems.

Diffussion Language Models for Music Generation

The goal of this research opportunity is to work within the CreativAI UC group to explore text diffusion models applied to symbolic music generation. In this internship, you will:
- Study diffusion models
- Implement and develop experiments to evaluate text diffusion models such as the recently released DiffusionGemma
- Attend weekly group meetings to stay up-to-date on topics within the Creative AI field https://creativai-uc.github.io/
(initially Tuesdays 12:30pm-1:30pm)

- Requirements:
* Advanced programming skills or equivalent
* Experience programming neural network models with PyTorch, Keras, TensorFlow, JAX, etc.

Rheological Analysis of Protein-Enriched Rice Flours for Extruded Snack Applications

This project aims to evaluate the rheological behavior of flours and starches obtained from different rice varieties enriched with plant proteins, in order to determine their potential as ingredients for the production of extruded snacks.

The experimental work will consist of measuring viscosity and viscoelastic properties using a rotational rheometer. Through these analyses, the study seeks to understand how protein incorporation modifies the behavior of rice starch and how these changes may influence key product attributes, such as expansion and texture of the resulting snacks.

This work is part of the doctoral thesis of Daniela Muñoz. The study is expected to generate rheological data that can be compared with results from third-generation extrusion studies and extrudate characterization, with the aim of determining the suitability of different rice varieties for the production of high-protein snack products.

The work is expected to be carried out during the second semester of 2026.

Multimodal integration in Alzheimer's Disease through Patient Similarity Networks

Patient Similarity Networks (PSNs) integrate heterogeneous data by turning each modality into a patient-by-patient similarity graph and fusing those graphs into one shared structure. The approach extends beyond omics to neuroimaging, clinical, and demographic data, yet multi-omics PSN work in Alzheimer's disease is still uncommon. In this project the student builds a PSN integration pipeline on real Alzheimer's data from ADNI, combining molecular and precomputed imaging features, and tests how the choice of distance metric, network fusion algorithm, and graph embedding shapes the patient subtypes it recovers. The goal is to find strata that single modalities miss, and to characterize them by diagnosis, APOE genotype, and cognition.

Corrosion-LAB: Failure prevention in national industry (2026-II)

CONTEXT: The reliability and useful life of industrial equipment depend to a large extent on the performance of the materials that make up their functional surfaces. Phenomena such as corrosion, wear and degradation generate significant economic losses, decrease the operational availability of assets and can lead to failures with relevant consequences for safety and productivity. In strategic sectors for Chile, such as mining, energy and agribusiness, there is a growing need to select suitable materials for the expected useful life and evaluate the causes of failures that occur early.

OFFER: We are looking for undergraduate students interested in articulating their knowledge for applied research carried out by the "Corrosion-LAB" team. The student can join with a focus on one of the current challenges (to be chosen):
(1) Design and construction of an experimental set-up for FAC (flow accelerated corrosion) resistance evaluation, (2) Development of protocols for fast and accurate analysis of metallic materials ("Materials Science"), (3) Development of protocols for evaluation of polymer performance in aggressive environments ("Materials Science").

Dedication: The 10 crs can be concentrated in one semester or distributed in 5 cr over two semesters.

Erosion-LAB: Wear of Materials by Erosive-Abrasive Flow (2026-II)

CONTEXT: Pulp processing equipment in industry often fails early, despite meeting design and construction standards. The research by the "Erosion-LAB" team addresses the need to ensure operational continuity and extend the useful life of systems that handle turbulent and abrasive flows, such as mining slurries.

OFFER: Collaborate in the development of an intelligent monitoring system that combines acoustic images with flow analysis and material characterization. The student can join our team, applying knowledge from various areas to (choose):
(1) Optimize the design and operation of the turbulent flow simulator (<--"Mechanical Design"),
(2) Develop wear models (<-- "Materials Science"),
(3) Analyze turbulent flow in 3D ("<-- Fluid Mechanics"),
(4) Analyze acoustic signals and images (<-- "Electrical circuits"),
(5) Automate the data acquisition system (<-- "Systems Control").

Dedication: The 10 crs can be concentrated in one semester or distributed in 5 cr over two semesters.

Possible extension as a professional internship (Aeonix) or a master's thesis.

Modelling remaining useful life of physical assets affected by corrosion (2026-II)

PROBLEM: The mining industry faces increasing challenges associated with the degradation of water pipelines due to corrosion, which impacts operational continuity and maintenance costs. In this context, there is a need to move towards strategies based on condition monitoring that allow estimating the remaining useful life (RUL) of assets.

OBJECTIVE: This research evaluates the applicability of selected degradation models on a set of data from industrial application, with the aim of identifying robust methodologies that include the uncertainty and change of conditions throughout the useful life of a pipeline.

Dedication: The 10 crs can be concentrated in one semester or distributed in 5 cr over two semesters.

Activities design for robotic manipulador

work on enabling the use of robotic arms using ROS and the design of educational activities focused on modeling, control, and algorithm implementation to support robotics courses. Research on collaborative activities between robotic arms, robot-robot and human-robot interactions

Can winter storms help restore eroding beaches? Remote sensing of beaches and inlets on the Chilean coast.

Remote sensing of the earth allows us to observe and quantify spatial and temporal processes. On the Chilean coast, beach erosion is increasing vulnerability of ecosystems and infrastructure to climate change, but this process is non-linear. Winter storms in 2023 and 2024 brought both freshwater and watershed sediments to the coast. This iPre will test the hypothesis that the 2023 and 2024 winter storms deposited litoral grade sediments on the Chilean coast, allowing for beach renewal.

The project will use remote sensing (e.g. CoastSat which uses Sentinel-2 and Landsat 8/9) to detect shoreline changes focusing on beaches with river mouths (Vos et al. 2019). This work will link to ongoing work at the Ligua-Petorca estuary where a camera at the inlet is also recording inlet and beach changes on a finer resolution.

The objective will be to test the hypothesis by looking at coastal shoreline changes, and relate them to oceanic and watershed processes.

This project requires programming skills (e.g. Coastsat is written in Python). Experience in GIS and time-series analysis is beneficial but not a requirement. This project may also include fieldwork to validate measurements.

References

Sánchez-García, E., Palomar-Vázquez, J. M., Pardo-Pascual, J. E., Almonacid-Caballer, J., Cabezas-Rabadán, C., & Gómez-Pujol, L. (2020). An efficient protocol for accurate and massive shoreline definition from mid-resolution satellite imagery. Coastal Engineering, 160, 103732.

Vos, K., Splinter, K. D., Harley, M. D., Simmons, J. A., & Turner, I. L. (2019). CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery. Environmental Modelling & Software, 122, 104528.

Reparation-LAB: Applicability of Selective Electrodeposition for Recovery of Mechanical Parts (2026-II)

CONTEXT: The useful life of mechanical components is conditioned by the progressive deterioration of their functional surface due to wear and/or corrosion, which leads to the loss of critical tolerances and compromises their operational performance. In large components, such as those used in mining machinery, the high costs of replacement, together with environmental considerations, motivate the development of repair technologies to restore surface topography and recover the part's functionality.

OBJECTIVE: To collaborate with the "Reparation-LAB" team in the development of the device for the selective electrodeposition (ELO-Brush) of metal alloys. In particular, the student can work in:
(1) Design, manufacture, and functional testing of ELO-Brush components, (2) Implementation of electrodeposition experiments, (3) Physical-chemical, mechanical, and morphological characterization of the recovered surfaces, (4) State-of-the-art analysis (literature) and state-of-the-art (market).

Dedication: The 10 crs can be concentrated in one semester or distributed in 5 cr over two semesters.

Semi-supervised automatic segmentation in cardiac magnetic resonance imaging

Medical image segmentation is a labor-intensive and time-consuming task, which presents a significant challenge in clinical practice. This is exacerbated by the scarcity of data manually annotated by experts, as the labeling of 3D volumes requires specialized personnel and extensive hours of work. In this context, artificial intelligence, and particularly semi-supervised learning, emerges as a promising solution: it allows for leveraging both a small set of labeled images and a larger volume of unlabeled images, reducing the reliance on manual annotations without sacrificing accuracy.
This work aims to utilize state-of-the-art neural network architectures, adapted for semi-supervised frameworks, to automatically segment various contrasts and 3D cardiac MRI maps, optimizing the annotation process and improving the model's accuracy even with limited labeled data.

Converting a MATLAB numerical simulation code to a compileable C code

Starting with a MATLAB code for a numerical simulation of heat transfer and fluid mechanics, the source code needs to be converted to native C code, including a GUI and the respective graphics libraries, so that it can be compiled and generate an executable file. Preferably for compilation on the Microsoft Windows operating system.

51 Study and analysis of biblical texts using LLMs

Starting with different LLMs, these must be trained with texts extracted from the Old and New Testaments, with the aim of asking various questions to the trained model and thus analyzing the answers it gives in relation to the answers contained in these same biblical texts.

Study using ANSYS Zemax of light ray projection onto a screen

Using ANSYS Zemax software, the aim is to study the projection of light emitted perpendicularly from each point of a certain 3D topology onto a flat screen. The goal is to be able to record the intensity of each ray of light that strikes the screen, based on its distance from the screen. This is intended to help analyze the image formed on the Shroud of Turin.

Mejoramiento de Revisiones de Diseño Basadas en BIM usando Asistentes Virtuales de Inteligencia Artificial (2026-2)

IoP para estudiantes de pregrado en un innovador proyecto de investigación que combina el modelado de información de construcción (BIM), la realidad virtual (VR) y la inteligencia artificial (AI). El objetivo es desarrollar y optimizar asistentes virtuales basados en técnicas de inteligencia artificial para mejorar las revisiones de diseño en la industria de Arquitectura, Ingeniería y Construcción (AEC). Los participantes tendrán la oportunidad de trabajar en áreas emergentes, como la recuperación de información basada en AI (IR), la integración de modelos de lenguaje como GPT, y la mejora de la interacción en entornos de realidad virtual.

El proyecto implica diseñar y desarrollar un asistente virtual que utilice inteligencia artificial para facilitar la recuperación de información desde datos BIM en un entorno VR, mejorando así la comprensión del diseño, la colaboración entre stakeholders y la eficiencia del proceso de revisión de diseño.

Fronteras de la IA Generativa: Arquitecturas de Agentes y Modelos Multimodales para Dominios de Alto Impacto (2026-2)

¿Te imaginas desarrollar sistemas de IA que puedan razonar, crear y colaborar como nunca antes? Este proyecto te invita a sumergirte en las fronteras de la inteligencia artificial generativa, explorando Modelos de Lenguaje Multimodal (LMM) y LLM a través de arquitecturas de agentes inteligentes. Inspirados en laboratorios disruptivos (OpenAI, Google DeepMind, Anthropic), exploraremos cómo revolucionar sectores como educación adaptativa, diagnóstico médico, ingeniería automatizada e industrias creativas. En este proyecto desarrollarás: Arquitecturas de agentes IA que integren LMMs y LLMs para resolver problemas complejos; Sistemas de razonamiento explicable y Prototipos funcionales que demuestren el potencial transformador en aplicaciones reales

¿Por qué este proyecto? No es solo académico: es tu oportunidad de prepararte para el futuro de la IA contribuyendo en dominios de alto impacto. ¿Listo para explorar donde la creatividad humana se encuentra con la IA generativa?

Innovation, AI Technologies, and Sustainability in Companies and Startups 2026-2

- Topic options: Innovation, digital transformation, and the use of artificial intelligence in companies; eco-innovation as a response to sustainability challenges; factors influencing the success of fintechs and technology-based startups; wage gaps and sustainability.
- Methodological options: Econometric analysis, analysis based on natural language processing, database construction, and systematic literature review.
- The specific topic and methodology will be discussed with the applicant during the interview. Each IPre addresses only one of the topic options and, at most, two methodological techniques.
- To apply, please send me a CV and "ficha académica" to gvaldesu@uc.cl by July 14.

Assessment of Learning-by-Doing Experiences in Timber Construction Education

This opportunity seeks to assess the educational experience associated with learning-by-doing activities for teaching timber construction systems. The student will support the design, implementation, and analysis of surveys applied to undergraduate students participating in hands-on timber construction activities. The work will focus on evaluating students’ perceptions, learning experience, engagement, and perceived development of technical and transversal skills. The results will contribute to the validation of active learning strategies in timber engineering and construction education, as part of a teaching-focused research project.

Experimental Research in High-Speed Electronics (GaN Transistors)

I am looking for an undergraduate student to join a research project focused on the development and experimental validation of next-generation power converters.

The Technical Challenge:
The work involves supporting the experimental development of active gate driving techniques applied to Gallium Nitride (GaN) transistors in a half-bridge configuration.
The main tasks will be associated with the design, assembly, and validation of a high-bandwidth current measurement stage, including the selection and characterization of sensors or shunt resistors, parasitic effect analysis, calibration of the measurement chain, synchronization with voltage and gate signals, di/dt estimation, and switching energy calculation. The work builds upon progress already made by a highly experienced Master's student, who will provide laboratory supervision.

Desired Profile
Essential Requirements: Demonstrable practical experience in a laboratory, oscilloscope operation, component soldering, and PCB assembly/modification.

What you DON'T need: No prior knowledge of Power Electronics is required. We are looking for experimental rigor, an interest in instrumentation and high-speed signal analysis; you will learn the specific theory here.

Concrete Futures: Technology and Modernization in Chile and Brazil (1955–1975)

How did reinforced concrete contribute to the materialization of sociotechnical imaginaries of the future in Chile and Brazil between 1955 and 1975? This Undergraduate Research Fellowship (IPRE) project proposes a historical and documentary study of reinforced concrete as a pivotal technology that shaped the urban, architectural, and cultural modernization of Latin America during the second half of the twentieth century. Although concrete has been extensively studied from technical and structural perspectives, a critical gap remains regarding its cultural dimension and its role in producing futures imagined through scientific progress and industrial development (Forty, 2012; Cohen, 2011). A comparative analysis between Chile and Brazil reveals how a single material was appropriated within distinct national contexts, uncovering diverse trajectories of articulating the future through the circulation of technical expertise and discursive practices. In the post-war era, concrete became the paradigmatic material for modern planning, the expansion of public infrastructure, and a widespread faith in the technical rationalization of territory. Furthermore, both nations represent critical case studies due to the intensity of their industrialization and urban planning, as well as their active participation in transnational networks of technological knowledge (Gorelik, 2005; Liernur, 2001). This comparative framework contributes to a Latin American genealogy of innovation, framed not as a linear technology transfer from global centers, but as situated processes of appropriation, translation, and resignification of development materials, techniques, and forms.This research operates at the intersection of the history of technology, material culture, innovation studies, and new materialisms. Methodologically, the study relies on the qualitative documentary analysis of historical archives and specialized architecture, engineering, and design journals published between 1955 and 1975. This corpus will allow for the reconstruction of innovation discourses, the mapping of technical knowledge networks, and the semantic deconstruction of the future as embedded in concrete. The empirical material will be systematized using conceptual profiles, content analysis, and digital data visualization tools.The project aims to advance the understanding of the genealogy of innovation in Latin America by shifting the scholarly focus from abstract definitions toward concrete material substrates. Additionally, it offers a critical reading of concrete as a technology that not only transformed the built landscape but also materialized the socio-political futures of developmentalist modernization. Furthermore, this research enhances engineering education by fostering a sociotechnical understanding of material innovations as phenomena that are simultaneously technical, aesthetic, political, and socio-environmental. Ultimately, the project contributes to the interdisciplinary reappraisal of twentieth-century concrete heritage.
Research Lines
Line 1: Literatur Rewiew. Development of an academic dossier that compiles, classifies, and critically analyzes specialized literature on concrete across the fields of innovation, material culture, history of technology, modernization, developmentalism, and technological optimism. Deliverables: Analytical index cards reviewing books, scientific papers, theses, journals, and relevant archival collections.Line 2: Digital Analytical Repository and Evidence ArchiveConstruction of a structured database consisting of analytical case profiles that link architectural, design, and artistic works executed in concrete with their primary documentary sources (including project files, historical photographs, blueprints, technical specs, and institutional records).Final Deliverables: Analytical case-study profiles and digital data visualizations mapping technological circulation patterns, timelines, actor-networks, and geographical clusters.Impact on Academic TrainingCultivation of interdisciplinary competencies that enable students to evaluate material innovations as multifaceted phenomena that operate simultaneously across technical, aesthetic, political, and socio-environmental registers.

Nuclear fusion and data analysis for a superconducting reactor

Are you interested in clean energy and data analysis? This project will allow you to work with real data from the EAST tokamak (China), a superconducting fusion reactor that contributes to the global ITER project. You will participate in an international collaboration analyzing emission spectra to study impurity behavior and improve magnetic confinement in the plasma. You will use programming tools in Python and Matlab, along with numerical modeling, to tackle one of the most important challenges in the future of energy.

Evaluation of the Anti-Evasion Plan

Fare evasion poses a major challenge to the financial sustainability, operational efficiency, and social legitimacy of public transport systems in cities worldwide. In Santiago, Chile, fare evasion has long been a persistent issue that impacts public transport revenue, increases the need for state subsidies, and fosters a sense of inequity between passengers who pay the fare and those who use the service without contributing to its funding. Given the complexity of the phenomenon, authorities have implemented various strategies over the past decade to curb evasion (the "Anti-Evasion Plan"), combining enforcement measures, technological upgrades, operational interventions, communication campaigns, payment incentives, and regulatory changes. However, there remains limited systematic evidence regarding the relative effectiveness of these interventions and the mechanisms through which they influence user behavior.
The Anti-Evasion Plan implemented within the Red Movilidad system comprises a set of actions aimed at reducing fare evasion through a comprehensive strategy. These actions include strengthening field inspections, increasing penalties for evasion, installing physical barriers and designated payment zones, incorporating validation and monitoring technologies, conducting user information and awareness campaigns, improving the system's operational design, and coordinating among the various public agencies responsible for transport management. While each of these measures pursues the same objective, their effectiveness can vary significantly depending on the operational context, route characteristics, user demographics, the socioeconomic conditions of the areas served, and how the different interventions interact with one another.

Against this backdrop, this project aims to develop a comprehensive methodology to evaluate the impact of the various measures making up the Anti-Evasion Plan, thereby generating evidence to guide the design of a more effective strategy for 2027 and beyond. Rather than merely assessing the plan's aggregate outcome, the research seeks to identify which components yield the greatest reductions in evasion, under what conditions they perform best, which offer the best cost-effectiveness, and how they can be combined to maximize their impact.
The project is based on the premise that fare evasion is a multidimensional phenomenon that cannot be explained solely by economic variables or the intensity of enforcement. Various international studies show that the decision to pay or evade also depends on factors such as social norms, the perception of fair pricing, service quality, the perceived likelihood of inspection, expected penalties, the ease of fare validation, urban environment characteristics, and user trust in the institutions responsible for transport. Consequently, any evaluation of the Anti-Evasion Plan must simultaneously consider operational, economic, behavioral, and institutional dimensions.

To address this challenge, the research will develop an evaluation methodology based on multiple information sources and a set of indicators capable of measuring the performance of each intervention from various perspectives. The proposal aims to integrate operational data from the Red Movilidad system, administrative records, fare validation data, enforcement statistics, demand indicators, financial data, and evidence from previous studies conducted both in Chile and abroad.

This research is being conducted in collaboration with the DTPM, and we will hold meetings throughout the semester with the department responsible for fare evasion.

Bibliometric map of the economics of critical minerals and the energy transition (2015–2025) + 2026–2028 agenda

Motivation: The literature on “critical minerals” has grown explosively over the last decade and is scattered across the fields of economics, public policy, engineering, sustainability, and geopolitics. A bibliometric map based on open data allows for structuring the field and identifying sub-communities (clusters), “bridge” papers, evidence gaps, and priority questions for the 2026–2028 agenda.
Expected results: The study aims to identify 4–8 dominant clusters and their temporal dynamics, as well as a small set of papers and authors acting as “bridges” between disciplines. The paper should conclude with a prioritized (high-impact) agenda for mineral economics and public policy.

Markerless Computer Vision-Based Gait Analysis for Objective Human Movement Assessment

Quantitative gait analysis is a key tool for functional assessment, clinical follow-up, and decision support in rehabilitation and sports medicine. However, traditional marker-based motion capture systems are costly, require specialized infrastructure, and are generally limited to laboratory settings. This project aims to develop and validate a markerless gait analysis platform based on conventional cameras and computer vision algorithms capable of estimating human kinematics without the need for body-mounted markers. The proposed platform will extract spatiotemporal and biomechanical gait parameters by integrating state-of-the-art pose estimation models and artificial intelligence techniques to improve the accuracy and robustness of the measurements. The system will be evaluated across different application scenarios, with the goal of providing a low-cost, scalable, and easily deployable solution for clinical, sports, and home-based environments. This initiative seeks to increase access to objective movement assessment tools and contribute to the advancement of digital health technologies for functional monitoring and personalized care.

Dynamic Analysis and Optimization Algorithms for the Control of Multivariable Systems

Many modern high-tech systems require controlling multiple critical variables simultaneously. Although this is traditionally addressed through cascade control schemes, current trends point towards Model Predictive Control (MPC) formulations, which consolidate regulation tasks into a single real-time optimization problem.

In this IPRE we will focus on analyzing the dynamic behavior of this type of formulations. The mathematical model of the system is already fully developed and available, so the student's work will consist of the theoretical characterization and simulation of the performance of the algorithms.

The project will address two main objectives:

System Dynamics Analysis: Study the properties of the resulting multivariable model, evaluating the couplings between states and control actions, as well as the linearization of the system.

Algorithm Evaluation (Solvers): Implement and compare different numerical optimization methods to evaluate the trade-off between mathematical precision, computational effort and feasibility of execution in real time.

Study application:
The practical case study where these tools will be evaluated corresponds to energy balance and current regulation in advanced power converters (used in smart grids, energy storage and electromobility).

Profile and Prerequisites:
Tools: MATLAB/Simulink or Python.

Prerequisites: Solid knowledge of Dynamic Systems and Automatic Control. Basic notions of mathematical optimization and programming.

Impact and Learning:
The student will develop advanced skills in the analysis of multivariable systems, formulation of optimization algorithms and simulation of predictive control strategies, skills highly transferable to both postgraduate research and technological development in the industry.

Assessing the Potential of the Feammox Process for the Biological Removal of PFAS from Contaminated Waters

This project will evaluate the potential of the Feammox process as a biotechnological strategy for the degradation and removal of per- and polyfluoroalkyl substances (PFAS), a class of highly persistent emerging contaminants. The study will investigate the ability of enriched microbial consortia to transform these compounds under anaerobic conditions coupled with ferric iron reduction.