Postdoctoral Researcher – Neural Architecture Search and Machine Learning

Disruption is the new normal, and at Tecnológico de Monterrey, every morning we wake up with the responsibility of transforming our surroundings. Our processes are people-centered, ensuring extraordinary experiences through our talent and the innovative Educational Model. Here, you’ll find your purpose, seize innovative opportunities, and be part of a collaborative culture based on inclusion, meritocracy, and respect.

What will you do?

We are seeking a highly motivated Postdoctoral Researcher to join the Research Group in Artificial Intelligence at the School of Engineering and Sciences, working in close collaboration with Prof. Raúl Monroy. The project develops a multi-objective Neural Architecture Search (NAS) approach to automate the design of Deep Neural Networks, combining evolutionary-based global search with domain-driven local search, machine learning, and knowledge-based reasoning. Image Restoration is used as the case study, given its relevance in security, medical imaging, and other settings where accuracy must be balanced against limited CPU power and memory storage. This is a full-time position for one year, with possible extension to a second year depending on performance, starting September 2026.

Your main challenges will be:

Design and develop multi-objective Neural Architecture Search methods that automate the design of Deep Neural Networks.
Combine evolutionary-based global search with domain-driven local search, integrating machine learning and knowledge-based reasoning to predict compliance with multiple objectives.
Investigate formalisms to represent the DNN search space so that search is driven toward architectures that are objective-compliant, novel, and inspiring.
Validate the approach on Image Restoration tasks, balancing performance and robustness against limited computing resources.
Collaborate with a multidisciplinary research team and contribute to the supervision of undergraduate and graduate student projects.
Publish research findings in high-impact journals and conferences.

What are we looking for?

PhD in Computing or a closely related scientific field.
Proven research track record in machine learning or multi-objective optimisation.
Experience in Neural Architecture Search, Deep Learning, Transfer Learning, or Natural Language Processing.
Strong publication record and the drive to innovate and make visible contributions in the field of ML, NAS, and NLP.
Excellent organisational and communication skills, written and verbal, in both Spanish and English.
Previous postdoctoral experience (an advantage, but not a must).
Previous teaching experience and supervision of undergraduate or PhD student projects (a beneficial asset).

Application requirements:

-    Detailed CV, list of publications, awards and recognitions, and potential start date.
-    Short statement of previous work, research interests and his/her future vision on the field.
-    Names and contact information of three referees.
-    PDF of a recent publication considered by the candidate as being representative of his/her research work.

Why work with us?

Our talent is the driving force behind our success. Their talent, dedication, and passion are the fundamental pillars of our organization. That’s why we focus on offering quality benefits that promote emotional balance, financial well-being, and professional development.

Contact: Karen Aguayo    Email: karenaguayo@tec.mx

At Tecnológico de Monterrey, we operate under the principle of equal opportunities. Therefore, we do not discriminate based on age, ethnicity, nationality, gender, sexual orientation, marital status, social condition, health status, religious beliefs, political doctrine, or disability.

Thank you for your interest in our job openings. If you are not contacted by the Talent Attraction team, we invite you to continue exploring new opportunities on the Growth Opportunities portal if you are a TEC collaborator. If you are an external candidate, visit jobs.tec.mx and apply to positions that match your profile.

https://tec.mx/es/profesores/unete
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