PhD in Scientific ML & Foundation Models

Delft; Delft; On-site; EUR 36,000 - 45,000; Full time

Job summary

TU Delft invites applications for a fully funded PhD position in Scientific Machine Learning (SciML), integrating data-driven ML with physical laws and domain constraints. The project focuses on scientific foundation models, inverse problems, and uncertainty-aware methods across subsurface, climate, energy systems, and more.

The candidate will join a multidisciplinary team at the Pattern Recognition Lab (PRLab) under Dr. Jing Sun.

Qualifications

• MSc degree in computer science, artificial intelligence, applied mathematics, applied physics, or a closely related field.
• Strong theoretical understanding of machine learning fundamentals with focus on methodological development.
• Basic knowledge of physical problems, especially inverse problems, and scientific applications.
• Strong programming skills (preferably Python).
• Ability to work independently and to collaborate effectively.
• Strong research communication and interpersonal communication skills.

Responsibilities

• Define, develop, and analyze scientific foundation models: large-scale, generalizable representations of physical systems.
• Explore inverse problems, uncertainty-aware methods, and cross-domain generalization for foundation models.
• Collaborate across machine learning, applied mathematics, and domain sciences in a multidisciplinary environment.
• Contribute to methodological advances and dissemination of results through publications.

Skills

Machine Learning

Python

Research communication

Independent work

Education

MSc degree in computer science, artificial intelligence, applied mathematics, applied physics, or a closely related field

Job description

TU Delft invites applications for a fully funded PhD position in Scientific Machine Learning (SciML), integrating data-driven ML with physical laws and domain constraints. The project focuses on scientific foundation models, inverse problems, and uncertainty-aware methods across subsurface, climate, energy systems, and more.

The candidate will join a multidisciplinary team at the Pattern Recognition Lab (PRLab) under Dr. Jing Sun.