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Universidad Rey Juan Carlos

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Fully Funded PhD Position in Generative Sampling for Stochastic and Bilevel Optimization Universidad Rey Juan Carlos in Spain

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded PhD position.

Deadline

Oct 31, 2026

Country flag

Country

Spain

University

Universidad Rey Juan Carlos

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Keywords

Computer Science
Electrical Engineering
Mathematics
Artificial Intelligence
Linear Algebra
Optimisation
Statistics
Physics
ML

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About this position

Fully funded PhD opportunity in Generative Sampling for Stochastic and Bilevel Optimization, with research activity linked to Universidad Rey Juan Carlos (Madrid, Spain) and École Polytechnique (Paris, France).

The post highlights supervision by Prof. Antonio G. Marques and Prof. Luiz F. O. Chamon, within the ELLIS network (ELLIS Madrid / ELLIS Paris). The project sits at the intersection of computer science, mathematics, statistics, and electrical engineering, with emphasis on optimization, probability, linear algebra, and modern ML tooling.

Eligibility: an MSc in EE, CS, applied math, statistics, or physics. Strong skills in optimization theory, probability, and linear algebra are required, together with practical programming experience in Python and PyTorch/JAX.

Funding: the position is explicitly described as fully funded.

Deadline: 31 October 2026.

Interested candidates should apply as soon as possible and ensure they can demonstrate the required quantitative background and coding experience relevant to stochastic optimization and generative methods.

Funding details

Fully funded PhD position.

What's required

Applicants should have an MSc in Electrical Engineering, Computer Science, applied mathematics, statistics, or physics. Strong preparation in optimization, probability, and linear algebra is required, along with Python and PyTorch/JAX skills.

How to apply

Apply now through the opportunity link or application process mentioned in the post. Prepare to demonstrate your MSc background, optimization and probability skills, and programming experience in Python and PyTorch/JAX.

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