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# PhD Position Scientific Machine Learning

- **Company:** TU Delft
- **Location:** Delft, Netherlands
- **Salary:** €3,059.0 - €3,881.0
- **Contract:** Temporary contract
- **Skills:** Artificial Neural Networks, Computer Programming, Python (Programming Language), Machine Learning, Deep Learning, Information Technology
- **Published:** May 21, 2026
- **Apply:** https://www.careerjet.nl/jobad/nl6f83d9c1f82f55f895960605b08e24a2

## About the Role

* 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, being organized) and to collaborate effectively.
 * Strong ability in research communication and interpersonal communication.

In addition, please note that 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. For more details, please check the Graduate Schools Admission Requirements.
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 pave the way for diverse career paths, inside or outside academia., * Diplomas/Degrees, including a Grade Transcript of previous education at the Bachelor and Master levels

You can address your application to Dr. Jing Sun.
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. For more details please check the

## Description

Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure?
No
Offer Description
Passionate about advancing foundation models for science? Join our PhD project at TU Delft!, 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.


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