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Anna Lukina

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Postdoc on Decision Trees for Control and Monitoring of Stochastic Systems Delft University of Technology in Netherlands

Degree Level

Postdoc

Field of study

Computer Science

Funding

Available

Deadline

Nov 23, 2026

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Country

Netherlands

University

Delft University of Technology

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Keywords

Computer Science
Electrical Engineering
Mathematics
Algorithm Design
Reinforcement Learning
Formal Methods
Combinatorial Optimization
Runtime Verification
Statistics
ML

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About this position

This postdoctoral project at Delft University of Technology focuses on decision trees for control and monitoring of stochastic systems. Building on the group’s recent algorithms for synthesizing optimal tree policies, the work aims to extend these methods to uncertain, real-world settings where deployed controllers must remain reliable when reality deviates from the model.

The research is centered on compact, human-readable decision trees that can support formal analysis and safer decision-making in safety-critical environments. Two main directions are highlighted: developing explainable runtime monitors that identify the environment in which a system is operating so the correct policy can be selected or policy guarantees can be checked, and improving the scalability and stochasticity of search-based synthesis so that decision-tree policies can be applied to stochastic and black-box systems with larger state spaces.

The postdoc will join the Algorithmics Group in the Department of Software Technology within TU Delft’s Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). The group works on intelligent decision-making, and the successful candidate will collaborate with PhD students and postdocs, with opportunities to co-supervise students. The project aims to produce results for leading venues in formal methods and AI, together with open-source tools.

The position is supervised by Prof. Anna Lukina at TU Delft. The broader faculty environment combines electrical engineering, mathematics, computer science, and applied research in a large, international technical university setting in Delft.

Funding details

Available

What's required

A PhD in computer science or a related field, or being close to completing one, is required. Applicants should have a background in formal methods, planning under uncertainty, or machine learning for sequential decision making, along with solid foundations in mathematics and logic and experience in algorithm design, combinatorial optimization, and implementation. A strong publication record relative to career stage is expected, ideally in venues such as CAV, TACAS, RV, AAAI, IJCAI, or NeurIPS. Excellent written and spoken English is required. Experience with uncertain or robust Markov decision processes, probabilistic model checking, reinforcement learning, decision trees, branch-and-bound search, mixed-integer programming, runtime verification, and strong programming skills in Rust, Python, or C++ are considered advantages.

How to apply

Apply via the TU Delft application button by 23 November 2026. Upload your academic CV, a motivation letter explaining your interest in the project and preferred research direction, and your PhD thesis or recent draft/two representative publications if not yet completed. Address the application to Prof. Anna Lukina.

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