Our recent algorithms synthesize optimal tree policies orders of magnitude faster than earlier approaches. Now we want to take them into uncertain, real-world settings. Job description Learned controllers are increasingly deployed in safety-critical settings, yet they offer few guarantees once reality deviates from the model they were trained on. Decision trees offer a promising alternative: they are compact, human-readable, and amenable to formal analysis.
This position builds on the recent results from our group. Possible directions include:
- Explainable monitoring: designing interpretable runtime monitors that identify which environment the system is operating in, so the right policy can be selected, or so that to detect when the deployed policy’s guarantees no longer hold.
- Scalability and stochasticity: extending search-based synthesis of decision-tree policies to stochastic and black-box systems and to larger state spaces, so that optimal tree policies remain within reach for realistic problems.
You will have the freedom to shape the direction within this scope. The aim is to publish in leading venues in formal methods and AI, along with open-source tools.
You will join the Algorithmics Group in the Department of Software Technology, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). You will collaborate with PhD students and postdocs working on intelligent decision-making and will have opportunities to co-supervise students. You will be supervised by Prof. Anna Lukina.
Job requirements You should have:
- a PhD in computer science or a related field (or be close to completing one), with a background in formal methods, planning under uncertainty, or machine learning for sequential decision making;
- solid foundations in mathematics and logic, plus experience with algorithm design, combinatorial optimization, and implementation;
- a strong publication record relative to career stage, for example, at venues such as CAV, TACAS, RV, AAAI, IJCAI, or NeurIPS;
- excellent written and spoken English.
The following are a plus:
- experience with (uncertain or robust) Markov decision processes, probabilistic model checking, or reinforcement learning;
- experience with decision trees, branch-and-bound search, mixed-integer programming, or runtime verification;
- strong programming skills (e.g., Rust, Python, C++).
TU Delft (Delft University of Technology) Working at TU Delft means contributing to solutions that really make a difference.
For over 180 years, we have been training engineers who make an impact worldwide in companies, government bodies, or as entrepreneurs. Our alumni turn knowledge into concrete solutions for the challenges of today and tomorrow.
These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what society needs now and in the future.
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.
Working at TU Delft means join an international community of professionals and students. Together, we create knowledge, innovations, and solutions that help move the world forward.
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. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: 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.
Click here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.
Conditions of employment - Duration of contract is 1 year. Temporary.
- A job of 36-40 hours per week.
- Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
- An excellent pension scheme via the ABP.
- The possibility to compile an individual employment package every year.
- Discount with health insurers on supplemental packages.
- Flexible working week.
- Every year, 232 leave hours (at 38 hours). You can also sell or buy additional leave hours via the individual choice budget.
- Plenty of opportunities for education, training and courses.
- Partially paid parental leave
- Attention for working healthy and energetically with the vitality program.
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 If you would like more information about this vacancy or the selection procedure, please contact Prof. Anna Lukina, via
[email protected] .
Application procedure Are you interested in this vacancy? Please apply no later than
23 November 2026 via the application button and upload the following documents:
- your academic CV,
- a motivation letter (max. [1–2] pages) explaining your interest in the project, which of the three research directions appeals to you most, and how your background fits it;
- your PhD thesis (or, if not yet completed, a recent draft or two representative publications).
You can address your application to Prof. Anna Lukina.
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