PhD Position Scientific Machine Learning, Toward Scientific Foundation Models

Passionate about advancing foundation models for science? Join our PhD project at TU Delft!

Job description

We invite applications for a fully funded PhD position in the area of Scientific Machine Learning (SciML), which integrates data-driven machine learning techniques with established scientific knowledge, such as physical laws, differential equations, and domain-specific constraints, to model, simulate, and understand complex systems. The project will explore modern SciML methods, such as physics-informed neural networks, neural operators (e.g., Fourier Neural Operators) and hybrid physics-ML approaches.

These models are expected to play a significant role in scientific domains and critical applications such as climate and geoscience, as well as the energy sector, for example subsurface modeling, seismic inversion, climate prediction, renewable energy forecasting, and power grid optimization.

Building on this, the project focuses on the definition, development, and analysis of scientific foundation models: large-scale, generalizable models trained across diverse scientific datasets that aim to capture the underlying principles of physical systems and can be adapted to a wide range of tasks.

Within this broad theme, the PhD project can take several possible directions. One direction is to develop scientific foundation models for inverse problems, moving beyond forward simulation toward tasks such as inferring hidden physical parameters, reconstructing unknown states, or identifying governing mechanisms from indirect or partial observations. Other possible directions include developing uncertainty-aware methods that can identify unreliable predictions and indicate where additional data would be most valuable; studying how such foundation models generalize across related but distinct physical settings, such as changes in boundary conditions, geometries, parameters, or forcing terms; and exploring their potential to accelerate or complement conventional numerical simulations.

The successful candidate will join a multidisciplinary research environment at the intersection of machine learning, applied physics, and domain sciences.

Job requirements

To be considered for the position, you will have:

• MSc degree in computer science, artificial intelligence, applied mathematics, applied physics, data science, or a closely related field.
• Good theoretical understanding of the fundamentals of machine and deep learning, with a strong interest in methodological development rather than only implementation and application.
• Basic knowledge and a keen interest in physical problems, especially inverse problems, and scientific applications.
• Strong programming skills, preferably Python.
• Ability to work independently, taking initiative and being organized, and to collaborate effectively.
• Strong ability in research communication and interpersonal communication.

In addition, doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis.

To thrive as a PhD candidate

It’s crucial to have a strong research mindset driven by curiosity and passion for your topic. Reflecting on your motivation for pursuing a PhD trajectory is essential, as this path involves unique challenges and uncertainties inherent to scientific exploration. Success requires dedication, adaptability, the ability to analyze complex problems, manage your time effectively, innovate and stay resilient under pressure. Combined with the ability and willingness to work independently and collaborate well, these qualities are indispensable for a fulfilling PhD journey. These experiences will build you as an independent researcher, expand your professional network, and pave the way for diverse career paths, inside or outside academia.

TU Delft as an employer

The successful candidate will work within TU Delft in a multidisciplinary research environment at the intersection of machine learning, applied physics, and domain sciences.

Faculty of Electrical Engineering, Mathematics and Computer Science

The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. The faculty contributes to sustainable electricity grids, develops future chips and sensors, lays foundations for software technologies including AI, and advances applied mathematics in areas such as disease process mapping and simulation of volcanic ash plumes. There is plenty of room for ground-breaking research, supported by innovative education, excellent labs and facilities, and a strong international position.

Conditions of employment

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1.5 year contract with an official go/no go progress assessment within 15 months, followed by an additional contract for the remaining 2.5 years assuming everything goes well and performance requirements are met.

As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.