PhD Position Scientific Machine Learning for Scientific Foundation Models

PhD Position Scientific Machine Learning for Scientific Foundation Models: 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, including physics-informed neural networks, neural operators, hybrid physics-ML approaches, and emerging foundation-model paradigms for scientific data.

Scientific machine learning is increasingly important in domains where observations are indirect, incomplete, expensive, or noisy, and where reliable models must respect the structure of the underlying physical system. For example, in subsurface investigation, one may aim to infer hidden geological or physical structures from measurements such as seismic, electromagnetic, or other indirect observations. Similar challenges also arise in climate and geoscience, energy systems, materials modelling, fluid dynamics, and other scientific and engineering domains where data-driven models must interact with physical knowledge.

Such problems raise fundamental machine learning challenges: how to learn from limited and heterogeneous data, how to combine data with physics-based models, how to solve inverse problems under uncertainty, and how to build models that generalize across different physical settings.

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 reusable representations of physical systems and can be adapted to a wide range of scientific 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, sensors, or forcing terms; and exploring their potential to accelerate or complement conventional numerical simulations.

The project is methodological in nature and is not restricted to one application domain. Application settings such as subsurface investigation, climate and geoscience, energy systems, and other complex physical systems may provide sources of inspiration and evaluation, but we are primarily looking for a candidate with a strong background in computer science, artificial intelligence, applied mathematics, applied physics, or a closely related field, and with a strong interest in developing new machine learning methods for scientific problems.

The successful candidate will join a multidisciplinary research environment at the intersection of machine learning, applied mathematics, physics-based modelling, 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, 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.

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 (Delft University of Technology)

At TU Delft, our people make the difference. With their knowledge and curiosity, our staff provide a high-quality education and conduct pioneering research that extends beyond the campus. You will have the opportunity to take the initiative, work with others, and grow as a professional.

Faculty of Electrical Engineering, Mathematics and Computer Science

The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines, including AI, applied mathematics, and computer science. There is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.

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.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from €3204 - €4051 gross per month, from the first year to the fourth year based on a fulltime contract (38 hours), plus 8% holiday allowance and an end-of-year bonus of 8.3%.

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.

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

Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands.

Additional information

This PhD position is positioned within the Pattern Recognition Lab (PRLab), part of the Computer Science department (specifically the Intelligent Systems Department) of the Delft University of Technology under supervision of Dr. Jing Sun and Prof. dr. Marcel Reinders.

• FTE: 1.
• Hours per week: 36-40.