PhD in Scientific ML for Foundation Models

Delft; Netherlands; Remote; EUR 36,000 - 45,000; Full time

Job summary

TU Delft invites applications for a fully funded PhD position in Scientific Machine Learning, blending data-driven methods with physical laws to model complex systems. The project explores physics-informed neural networks and scientific foundation models for inverse problems.

You will join the Pattern Recognition Lab in the EEMCS faculty, collaborating with experts in ML, applied mathematics, and physics to advance rigorous research, software, and publications.

Qualifications

• MSc in CS/AI/Math/Physics or related field.
• Strong theoretical understanding of ML fundamentals with methodological development focus.
• Basic knowledge of physical problems and inverse problems.

Responsibilities

• Conduct research on SciML and foundation models for scientific data.
• Develop methods for inverse problems and uncertainty handling.
• Collaborate across ML, applied maths and physics groups and publish results.

Skills

Python

Independent work

Research communication

ML fundamentals

Inverse problems knowledge

Physics-informed ML

Education

MSc degree in CS/AI/Mathematics/Physics or related

Job description

TU Delft invites applications for a fully funded PhD position in Scientific Machine Learning, blending data-driven methods with physical laws to model complex systems. The project explores physics-informed neural networks and scientific foundation models for inverse problems.

You will join the Pattern Recognition Lab in the EEMCS faculty, collaborating with experts in ML, applied mathematics, and physics to advance rigorous research, software, and publications.