PhD Position Scientific Machine Learning for Scientific Foundation Models

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, 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.

Building on this motivation, 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.

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 (https://www.tudelft.nl/onderwijs/opleidingen/phd/admission).

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 (via jing.sun@tudelft.nl), and Prof. dr. Marcel Reinders.

Application procedure
Are you interested in this vacancy? Please apply no later than 29 Sep 2026 via the application button and upload the following documents:

• CV
• Motivational letter (no more than two pages) outlining your interest in pursuing a PhD and this particular project, as well as your previous research/work experience.
• 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 Graduate Schools Admission Requirements.

Please note:

• You can apply online. We will not process applications sent by email and/or post.
• As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology. The assessment is based on information provided by the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed. The processing of personal data in the context of the risk assessment is carried out on the legal basis of the GDPR: performing a public task in the public interest. You can find more information about this assessment on our website about knowledge security.
• Please do not contact us for unsolicited services.