EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than 6,500 people supporting the three main missions of the institutions: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of more than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries.

PhD - Multimodal Generative AI for Materials Design

Mission

EPFL is one of the most dynamic university campuses in Europe, ranks among the top 20 universities worldwide and offers an exceptional working environment with very competitive salaries. The LTS2 Lab https://lts2.epfl.ch offers a highly    motivating, interdisciplinary scientific environment with many opportunities to interact between different projects and researchers, and has an excellent network of collaborative research projects with applications ranging from biology to neuroscience. The objective of this project is to develop groundbreaking generative AI methodologies for the design of novel materials. This is part of a large ERC Synergy project involving de novo simulations, automated experimental platforms driven by AI models. It offers extraordinary opportunites to collaborate with materials scientists on a quest to set-up a new blueprint for materials discovery, from computational approaches to actual synthesis and characterization.

Profile

We are looking for 2 PhD students who will contribute novel multimodal generative AI architectures, latent diffusion processes, and reinforcement learning approaches. Interestingly, the proposed model will interface with an automated platform performing experimental materials synthesis and incorporate data from experimental characterization and simulations.

We are looking for PhD candidates with a strong analytical background, and an outstanding MSc degree in Engineering, Computer Science, Physics, Applied Mathematics, or a related field. You should be proficient in or willing to learn generative deep learning – in particular based on latent diffusion, multimodal approaches, statistics and learning theory. We expect the candidate to be self-driven with strong problem-solving abilities and out-of-the-box thinking. You are willing to contribute and understand new AI architectures, implement and test them. More importantly, you are willing to partake in a collective international research endeavour involving researchers from different disciplines. Professional command of English (both written and spoken) is mandatory. To be eligible for these positions you must also apply and be accepted to the doctoral program in Electrical Engineering. Please check https://www.epfl.ch/education/phd/ for additional information. Please note that this is a separate application process.

Main duties and responsibilities

As a PhD Student, you will be expected to:

Have full responsibility for your own dissertation

Research in close collaboration with academic partners;

Experiment design and execution ;

Analyze and interpret experimental results;

Write scientific articles for publication in peer-reviewed journals;

Present at international conferences.

Supervise student projects

We offer

4 years to complete your PhD with a competitive remuneration

A world-class research and training environment with access to state-of-the-art research facilities;

A multi-cultural and stimulating work environment;

International collaboration and research internships at other institutions in Belgium and France;

Term of employment: 1-year fixed-term contract (CDD), renewable for 4 years.

Informations

Only applications submitted through the online platform are considered.

Your application should contain:

  • Motivation Letter
  • Detailed CV
  • Contact information
  • At least 2 references willing to write a recommendation letter.

For more information, please contact: pierre.vandergheynst@epfl.ch

Activity Rate : 100.00

Contract Type : PhD Student

Reference : 2048