PhD - Exploring the geometry of Markov diffusion processes to design more efficient AI algorithms

École polytechnique fédérale de Lausanne, EPFL; Lausanne; Vor Ort; CHF 60’000 - 100’000; Vollzeit

Zusammenfassung

Join a prestigious institution as a PhD student exploring the geometry of Markov diffusion processes to enhance AI algorithms. This role offers a unique opportunity to engage in groundbreaking research with applications in GenAI and molecular biology. You will be part of a vibrant community, collaborating with top researchers and gaining access to world-class facilities. With a competitive remuneration package and a multicultural environment, this position is perfect for self-motivated individuals eager to make significant contributions to the field. Embrace the challenge and advance your academic career in an inspiring setting.

Leistungen

Access to world-class research facilities

Multicultural workplace

Competitive remuneration

Training environment

Qualifikationen

• Strong analytical background with an outstanding MSc in relevant fields.
• Proficiency in geometric deep learning and strong problem-solving skills.

Aufgaben

• Take full responsibility for your dissertation and collaborate with partners.
• Design and conduct experiments, analyze data, and publish findings.

Kenntnisse

Geometric Deep Learning

Signal Processing

Statistics

Learning Theory

Problem-Solving Skills

Ausbildung

MSc in Engineering

MSc in Computer Science

MSc in Physics

MSc in Applied Mathematics

Jobbeschreibung

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PhD - Exploring the geometry of Markov diffusion processes to design more efficient AI algorithms

Mission

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 education, research, and innovation. The campus offers an exceptional working environment within a community of over 18,500 people, including more than 14,000 students and 4,000 researchers from over 120 countries.

The LTS2 Lab (https://lts2.epfl.ch) provides a highly motivating, interdisciplinary scientific environment with numerous opportunities for collaboration across projects and with researchers, with applications ranging from biology to neuroscience.

The project aims to develop novel methodologies based on geometrical approaches, especially Markov diffusion processes, which could significantly impact techniques like diffusion models or Transformers prevalent in modern GenAI approaches. There will be opportunities for theoretical work and practical applications, for example in GenAI for molecular biology.

We are offering two fully funded PhD positions supported by the Swiss National Science Foundation.

Profile

Ideal candidates will have a strong analytical background with an outstanding MSc in Engineering, Computer Science, Physics, Applied Mathematics, or related fields. Proficiency or willingness to learn geometric deep learning, signal processing, statistics, and learning theory is required.

The candidate should be self-motivated, with strong problem-solving skills and innovative thinking.

Fluent English (both written and spoken) is mandatory.

Applicants must also apply and be accepted into the doctoral program in Electrical Engineering. Please refer to the provided webpage for more information. Note that this is a separate application process.

Main duties and responsibilities

As a PhD student, you will:

• Take full responsibility for your dissertation
• Collaborate closely with industry and academic partners in research
• Design and conduct experiments
• Analyze and interpret experimental data
• Publish scientific articles in peer-reviewed journals
• Present research at international conferences

We offer

• A 4-year period to complete your PhD with competitive remuneration
• Access to world-class research facilities and training environment
• A multicultural and stimulating workplace
• Employment term: 1-year fixed-term contract, renewable for up to 4 years

Informations

Only applications submitted via the online platform will be considered.

Your application should include:

• A detailed CV
• Contact information
• At least two references willing to provide recommendation letters

For more information, contact: [e-mail]