MISTI Mexico

Instituto Politécnico Nacional (IPN)

Centro de Investigación en Computación (CIC, Center for computing research)

www.cic.ipn.mx

Mexico City

The Government of Mexico City has about 9,000 bicycles that are available to people for a small annual fee. There are about 200 parking stations for these bikes, where you can take one, ride it to another part of the city and leave the bike there. These vehicles need to be taken from parking stations that are almost full of bikes, to near-empty parking stations, using a set of about 20 trucks that transport up to 40 bikes each, and another 20 trucks that transport up to 20 bikes each.

The problem to be solved is to design a close-to-optimal strategy to move the trucks, seeking to maximize the availability of bikes to passengers. Care needs to be given to transport time, vehicle capacity, time of the day, and expected ideal number of bikes at each parking station.

We have large quantities of data about the daily movement of the bikes, dating back to several years. The data base is kept up to date.

The intern is expected (1) to provide a paper solution to this problem; (2) to carry out its programming to completion; (3) to test the solution with real data; and (4) to document the work performed during his(her) stay.

The intern should be fluent in at least two computer programming languages and have good domain of data base technology. Acquaintance with free software tools is useful; it is also useful to build its own software tools, or glue code, as needed. Large quantities (20 megabytes, say) of data are normally processed. Some knowledge of statistics and maximization is useful. I prefer, but do not require, some knowledge of machine learning tools.

Jaime Peña Studio

LIB - Laboratorio de Investigación con Materiales Biogénicos

jaimepenastudio.com

Bacalar, Mexico

Jaime Peña Studio is a collective of creative minds inspired by the essence and magic of nature and the multiverse, using these elements to guide the creation of architecture aligned with planetary energy. Formed as a collaborative family, we operate within a fractal system to share knowledge widely, nurturing young creative talent and offering tools to support their growth and learning. The studio is the culmination of 20 years of research into bamboo and natural materials, applied to architectural and engineering projects. Our team includes architects, computational designers, and interior architects who blend handcrafted techniques with advanced technologies, merging digital and manual design processes.

Our aim is to create architecture that harmonizes with its natural surroundings, crafting spaces that are truly aligned with our planet. Currently, our 10-person team is based in a holistic center in the Magical Town of Bacalar on the Riviera Maya, home to a stunning lake of seven colors. We are looking for people who are passionate about innovation and nature, with an investigative and proactive spirit. Our office is a place of research, knowledge exchange, and personal interaction. We are always aiming to grow as professionals and individuals, placing a high priority on human connections. Knowledge in Rhino, Grasshopper, and visual programming is a requirement.

During this period, students/interns will assist in developing lightweight, three-dimensional bamboo structures, exploring structural strategies through simulations and programming inspired by nature and fractal systems. They will also contribute to researching and creating roofing finishes made from natural materials for bamboo structures. The aim is to apply and test these insights and contributions in real-world projects undertaken by the studio

The student’s role involves researching and developing strategies for bamboo structural designs, with a focus on connection methods, optimization, and structural simplification. Additionally, they will explore and test potential waterproof materials for coverings. Students should be proactive and solution-oriented, with proficiency in digital modeling and programming tools like Rhino and Grasshopper; programming skills are an advantage. They should be collaborative, open to knowledge-sharing with the team, and adaptable in both digital and hands-on work, including performing computational simulations and material testing.

Tecnologico de Monterrey

Smart Electronics Research Group

tec.mx/en/research/innovation-in-smart-digital-technologies-and-infrastructure

Monterrey, Mexico

The lure of quantum computation stems from its potential to solve problems considered intractable with current, classical computational resources, by exploiting principles of superposition and entanglement to enable speedups for certain classes of problems. The realization of this capability is still in the research and development stages, however, and quantum technologies at present remain firmly embedded in the Noisy Intermediate-Scale Quantum (or NISQ) era. 

The NISQ era of quantum computing is defined by one of the main limiting factors in quantum computing: the presence of noise across the system, which introduces errors in the desired quantum computation. While quantum error correction is the leading candidate method for achieving full-scale, fault-tolerant quantum computation in the long-term future, the resource requirements of quantum error-correction (in terms of the number of physical qubits and their error rates) are currently prohibitively high. Specifically, true quantum error-correction mechanisms require several additional qubits to maintain the information of a single qubit – in an approximately 1000:1 proportion, typically. Despite many advances in this area, true error-corrected and fault-tolerant quantum computing is still estimated to be a decade or so away. Consequently, it is of intense current interest whether some form of quantum advantage, or quantum utility, can be achieved with NISQ devices, without full quantum error correction. This is also an area of interest for both Dell and ASU. 

Quantum error mitigation (QEM) has emerged over the past decade as a method for reducing errors in quantum computations. Instead of full quantum error-correction, QEM algorithms aim to reduce errors in quantum computation via classical post-processing of the noisy measurement outcomes. Recent work has identified striking resource requirements (in terms of the number of quantum circuits executions, or “shots”) for effective error mitigation; however, these results hold in the worst case, i.e., for classes of circuits that are highly entangling and spread quantum information rapidly among the qubits. Error mitigation techniques may still be efficient for certain more realistic, problem-specific classes of circuits, and developing efficient QEM algorithms remains an active area of research. 

Machine Learning (ML) has been used in several parts of the quantum computing pipeline to help improve the performance of quantum algorithms. In the area of transpilation, for instance, where the original algorithm is translated into another representation that satisfies the constraints of the target hardware, ML has been used to identify the best qubit mapping and operation ordering to avoid noise in the computation. In the area of quantum algorithm search, where ideal quantum circuits are searched in a combinatorial space, ML has been used to eliminate the majority of poor candidates without having to execute them on the target quantum machine.

Encouraging results in these other areas of quantum computing inspire the investigation into how ML can help mitigate errors inherent to quantum computations. We hypothesize that, by capturing characteristics of quantum algorithms and quantum machines, a ML model could learn to associate them with the occurrence and propagation of noise across the circuit and how it affects the output. By understanding those relationships, we further hypothesize that it is possible to learn how to “denoise” the result in classical post-processing. Indeed, supervised ML excels at finding relationships between data characteristics and observed outcomes. Recent advances in denoising processes, especially through the advent of the diffusion models, have also demonstrated incredible capabilities in image and audio domains.

This project aims to fill the gap related to the lack of an evaluation framework to compare QEM approaches and, more specifically, ML-QEM approaches. Our intent is to develop a set of tools that enable quantum researchers and software developers to assess the performance of (ML-)QEM in a holistic and consistent manner.

Our goal is to:
(1) understand the advantages/disadvantages of the various approaches of ML-QEM, particularly for certain types of problems, noise models, circuit structures, etc.; 
(2) based on the previous point, provide practical guidance regarding what ML-QEM techniques work best for given sets of problems, and why. 

Our assessment tools should be extensible and pluggable, so that new (ML-)QEM approaches and benchmark datasets can be easily added.

CICATA Queretaro - IPN

Queretaro, Mexico

www.cicataqro.ipn.mx

This project focuses on developing a predictive model for soil moisture using advanced deep-learning techniques and remote sensing data. The model will predict soil moisture for up to 72 hours before adapting to diverse soil types and environmental conditions. Historical data from satellite missions like CYGNSS, SMAP, GOES, and Sentinel, combined with in-situ measurements, will enhance prediction accuracy and achieve a coefficient of determination (R²) above 0.88. The model's performance will be validated across regions with varying climates, including the USA, Spain, and Australia, providing insights for agriculture, hydrology, and climate change mitigation​.

The intern will focus on developing and implementing machine learning models to predict soil moisture using remote sensing data from satellite missions such as CYGNSS, SMAP, GOES, and Sentinel. Their primary responsibilities will include preprocessing large datasets, designing and training predictive models, and validating results across diverse climates in regions like the USA, Spain, and Australia. By achieving accurate soil moisture predictions, the intern will directly contribute to advancing our organization's efforts in climate resilience, precision agriculture, and hydrology. This work will provide actionable insights for sustainable land and water management, aligning with our mission to mitigate climate impacts through innovative technology.

Required skills for this project include proficiency in Python programming and familiarity with machine learning frameworks such as TensorFlow or PyTorch. Students should have experience working with large datasets and basic knowledge of remote sensing or geospatial data analysis. Preferred skills include prior exposure to time series analysis, deep learning architectures, and satellite data processing. Strong problem-solving abilities, attention to detail, and an interest in climate resilience or environmental sciences will also be valuable for successful project participation.

CICATA Queretaro - IPN

Queretaro, Mexico

www.cicataqro.ipn.mx 

This project focuses on identifying and tracking tropical cyclones using Machine Learning and remote sensing techniques. Students will develop models capable of predicting hurricane trajectories, destinations, and intensities with a minimum three-day window and a mean track error of 140 km. The work integrates CYGNSS (Cyclone Global Navigation Satellite System) observations and the International Best Track Archive for Climate Stewardship (IBTrACS). The ultimate goal is to enhance early warning systems and disaster planning by modeling the societal impacts of hurricanes on critical infrastructures, inhabited areas, and sensitive ecosystems.

Using remote sensing data and historical hurricane archives, the intern will design and implement machine learning models to predict tropical cyclone trajectories, intensities, and societal impacts. Their tasks will include data preprocessing, feature engineering, and model validation to achieve high prediction accuracy with low mean track errors. By contributing to developing these predictive systems, the intern will support the organization’s mission to enhance disaster preparedness and resilience planning. This work will directly benefit communities by improving early warning systems and minimizing the impacts of hurricanes on infrastructure, populations, and ecosystems.

Required skills for this project include proficiency in Python programming, a strong understanding of machine learning techniques, and experience working with geospatial or remote sensing data. Students should also know data preprocessing, feature engineering, and model evaluation. Preferred skills include familiarity with time series analysis, deep learning frameworks such as TensorFlow or PyTorch, and handling large datasets from sources like CYGNSS and IBTrACS. Additionally, a background in environmental science, meteorology, or disaster management, strong problem-solving abilities, and attention to detail will be valuable for successful project participation.

CICATA Queretaro

www.cicataqro.ipn.mx

The spatiotemporal analysis of human settlements in natural protected areas (ANPs) leverages a Geographic Information System (GIS) driven by machine learning techniques, utilizing satellite imagery from Landsat and Sentinel. This project aims to improve the spatiotemporal assessment of human settlements within 25 ANPs in Mexico City. By automating analysis and providing comprehensive coverage, our objective is to offer a cost-effective alternative to traditional field surveys, enabling timely and informed decision-making for environmental conservation.

Currently, the existing system effectively monitors human settlements. However, we aim to enhance its capabilities further by integrating more advanced algorithms and incorporating additional sources of information. These improvements will enable the system to deliver more accurate, granular, and timely insights, approaching real-time analysis of these phenomena.

The intern will be responsible for various tasks across data processing, system optimization, and reporting. They will assist in pre-processing satellite imagery, such as Landsat and Sentinel data, ensuring its quality for analysis, and implementing machine learning algorithms to extract meaningful features related to human settlements. Additionally,  they will focus on optimizing predictive models to enhance accuracy and integrate supplementary data sources to enrich the system's outputs. The intern will also develop intuitive dashboards and visualizations to showcase spatiotemporal trends and contribute to regular reports summarizing insights and progress for stakeholders.

Required skills for this project include proficiency in Python programming and familiarity with machine learning frameworks such as TensorFlow or PyTorch. Students should have experience working with large datasets and basic knowledge of remote sensing or geospatial data analysis. Preferred skills include prior exposure to time series analysis, deep learning architectures, and satellite data processing. Strong problem-solving abilities, attention to detail, and an interest in climate resilience or environmental sciences will also be valuable for successful project participation.

Grupo Kreactiva/ Urbanística

https://www.urbanistica.mx/

At Urbanística, we design innovative, customized, and sustainable solutions to address the urban challenges of the future today. We are a transdisciplinary and intercultural team focused on crafting tailored solutions for complex issues that impact people, cities, and the environment.

Over the years, we have collaborated on more than 110 projects with governments, businesses, and organizations across 8 countries and over 40 cities, creating a positive impact on urban and natural environments. We invite you to explore our projects and join us in this transformative vision.

Our founder is currently in their second year as a SPURS-MIT Fellow, and we would be delighted to welcome internships!

Goals:

Develop a study for the Protection of Areas with Historical, Artistic, Architectural, and Cultural Heritage Value by identifying and characterize the attributes within areas of historical, artistic, architectural, and cultural heritage value, highlighting their uses and purposes, as well as the main constraints for urban actions. These actions aim to ensure the conservation and enhancement of historical, artistic, architectural, and cultural heritage in the study area, in compliance with cultural heritage and urban development regulations at the Historical Center of Chihuahua, Chihuahua, Mexico.

- Assist in identifying and digitizing the official cartography of the ZVPHAAC (Zonal Area for Cultural and Heritage Protection).
- Collect and analyse information on the road structure, green spaces, public areas, and heritage buildings.
- Support in gathering data on the social, economic, and cultural characteristics of the population.
- Help monitor media related to cultural and urban heritage.
- Actively participate in tasks related to research, analysis, and systematization of information, contributing fresh ideas, and collaborating in the implementation of strategies to protect and promote cultural heritage in the study area.

Skills:

- A basic understanding of urban development, green spaces, public areas, and heritage buildings would be beneficial. 
-The ability to collect relevant data on social, economic, and cultural characteristics of populations.
- Proficiency in digital tools, use Office 365 software and ideally Geographic Information Systems (GIS).
- Since the intern will be working alongside professionals and other stakeholders, the ability to collaborate, contribute ideas, and work as part of a team.

Universidad Panamericana, Faculty of Engineering

Aguascalientes

www.up.edu.mx

Project:

The previously built prototype is based on the generation of a pulsing electrical field which allows certain foods to be processed in a better way to preserve them for longer without using pasteurization techniques. By the date of integration of the students, the prototype will consist of an interface to be able to improve its performance and efficiency by designing control algorithms. The algorithms will be implemented probably using an FPGA card and thus sample and control many aspects of food processing using the pulsed electric field.

Goals:

Electrical characterization of food samples, design of algorithms to improve the performance of a previously built prototype and writing of project reports to be part of a scientific publication.

Requirements:

Engineering students in electronics, mechatronics, bioelectronics or related engineering with more than 70% of their academic program covered. That they have knowledge in circuit design and simulation. Know how to use software for analysis of systems and circuits such as MATLAB, PLECS, PSIM or similar.

Escuela Superior de Ingeniería Mecánica y Electrica Unidad Culhuacan (School of Mechanical and Electrical Engineering, Campus Culhuacan), Sección de Estudios de Posgrado e Investigación, Laboratorio de Investigación en Calidad y Conversión de la Energía Eléctrica (LINC2E2)

Mexico City

https://www.esimecu.ipn.mx/ 

The project looks for techniques specialized in magnetics, mechanics and thermal areas in order to improve the gravimetric power density of medium-power, magnetic devices used in power electronic converters. It is desirable, but not obligatory, to have basic knowledge in 3D design, finite element computing and power electronics fundamentals.

The aim of the project is to aid for design strategies that overcome the bottleneck of reducing the size and volume of magnetic devices for power converters.

Looking for last year undergraduate students, postgraduate students.

Altor Finanzas e Infraestructura

Mexico City

https://www.altorcb.com/

These practices are designed to investigate artificial intelligence, taking advantage of state-of-art techniques to apply to infrastructure projects, asset refinancing, and market, credit and liquidity risks in lending and other financial activities.

Keywords: mathematics, modeling, statistical learning, probability analysis, data miniing, machine learning

Institute for Transport and Development Policy Mexico (ITDP Mexico)

Mexico City

itdp.org/mexico

ITDP is a global organization at the forefront of innovation, using technical expertise, direct advocacy, and policy guidance to mitigate the impacts of climate change, improve air quality, and support prosperous, sustainable, and equitable cities.

ITDP Mexico has very young and dynamic team, deeply committed to improving urban environments around the world. The culture of the office is very warm and laid back.

This project involves the analysis and evaluation of mobile phone data, using Big Data methodologies. The results will be useful to inform the spatial and operational planning of major interurban rail and mass transport systems in Mexico.

Experience with big data analysis and programming is required.

Spanish is preferred, as it would make collaboration with the team easier and more streamlined.

Instituto Politécnico Nacional

Network and Data Science Laboratory

https://www.cic.ipn.mx/

Mexico City

The main objective is to mathematically analyze and study different communications systems, focusing on reducing the energy consumption required to provide an acceptable system performance. We will focus on different systems such as vehicular networks, drone-assisted communication systems, body area networks, wireless sensor networks, Peer to Peer networks, and cellular systems. As opposed to conventional communications systems, where the main objective is to provide an acceptable Quality of Service (QoS) we are interested on providing alternatives to reduce energy consumption to mitigate the climate change problematic, as well as reducing electronic pollution produced by the constant usage of batteries. 

Goals: Generate mathematical models that allow a careful design of modern communication systems with low energy requirements.  Numerical simulations will validate the analytical results.

Requirements: Basic programming skills and basic probability concepts are required.