Job description

Offline reinforcement learning (RL) seeks to derive effective sequential decision-making policies exclusively from pre-existing datasets, circumventing the need for potentially costly, risky, or time-consuming online environment interaction. This paradigm holds significant promise for leveraging large data repositories in domains such as healthcare, autonomous systems, and robotics. However, existing offline RL methods often rely on kernel or neural approximators whose inductive biases are poorly matched to Q-function geometry, resulting in slow convergence and sample inefficiency. This project proposes a novel approach to offline RL by utilizing tropical (i.e., max-plus) kernel-based function approximation for the Q-function. The core motivation stems from the inherent structural compatibility between the Bellman operator, central to dynamic programming and RL, and the operations of max-plus algebra. To overcome the potential representational limitations of purely max-plus-linear functions, we integrate kernel methods, which implicitly map data to high-dimensional feature spaces, allowing for richer, non-linear approximations.

In this project, you will establish the theoretical foundation and design scalable algorithms for tropical kernel-based offline RL. You will start with identifying MDP classes whose optimal Q-function lies in tropical function spaces and developing representer-type theorems for kernel-based approximation within these spaces. You will also address the computational challenges inherent in kernel methods in tropical function spaces (e.g., model-size explosion and quadratic constraint growth) by devising tropical analogues of scalable kernel approximation techniques and developing efficient optimization solvers for the resulting regression problems.

Teaching activities are part of your PhD trajectory and may include, for example: Supervising workgroups or lab sessions, assisting in courses, or mentoring BSc and MSc students. While teaching will not be your main responsibility, it offers valuable experience that supports your development and prepares you for future academic or professional roles. Teaching activities will not exceed 20% of your total appointment, averaged over the course of your PhD.

Job requirements

The position is well-suited for candidates who sit at the intersection of theoretical machine learning, reinforcement learning, and optimization. The candidates are expected to carry out theory-driven RL research using kernels, optimization, and tropical methods. Therefore, a solid mathematical background and, more importantly, the willingness to further develop it as the project requires is a must-have. This position is not suited for those with main interest is purely empirical deep RL benchmarking without the appetite for theoretical work. To be precise, here are the minimum requirements for this position:

• A relevant MSc degree in systems and control, computer science, engineering, applied mathematics, or a related field.
• Solid mathematical background, i.e., comfort with linear algebra, analysis, probability, optimization, and ideally some functional analysis or approximation theory.
• Experience with (convex) optimization and algorithm design.
• Experience with kernel methods is a plus but not required.
• Experience with tropical methods is not required, but an interest in this aspect is important.
• Excellent command of the English language and communication skills.

TU Delft (Delft University of Technology)

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty Mechanical Engineering

From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its underlying mechanisms, research and education at the ME faculty focusses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems.

ME is a dynamic and innovative faculty with high-tech lab facilities and international reach. It’s a large faculty but also versatile, so we can often make unique connections by combining different disciplines. This is reflected in ME’s outstanding, state-of-the-art education, which trains students to become responsible and socially engaged engineers and scientists. We translate our knowledge and insights into solutions to societal issues, contributing to a sustainable society and to the development of prosperity and well-being. That is what unites us in pioneering research, inspiring education and (inter)national cooperation.

Click here to go to the website of the Faculty of Mechanical Engineering. Do you want to experience working at our faculty? These videos will introduce you to some of our researchers and their work.

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 €3059 - €3881 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

For more information about this vacancy, please contact Dr. M.A. Sharifi Kolarijani, m.a.sharifikolarijani@tudelft.nl.
For more information about the application procedure, please contact G.D. Kocken, HR advisor, recruitment-me@tudelft.nl.

Application procedure

Are you interested in this vacancy? Please apply no later than 30 April 2026 via the application button and upload the following documents:

• A curriculum vitae (CV) that states your education and relevant working experience.

• A brief research statement describing your interest and background in the topic (no more than 1 page).

• One or two research-oriented documents written by the applicant (e.g., MSc thesis, journal/conference publication, project report).

• Transcript for your BSc and MSc degrees including grades for courses.

You can address your application to Dr. M.A. Sharifi Kolarijani.

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.