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# Postdoc on Decision Trees for Control

- **Company:** TU Delft
- **Location:** Delft, Netherlands
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Algorithm Design, C++ (Programming Language), Computer Programming, Integer Programming, Python (Programming Language), Machine Learning, Open Source Technology, Reinforcement Learning, Rust (Programming Language), Information Technology, Formal Methods, Markov
- **Published:** November 6, 2026
- **Apply:** https://www.academictransfer.com/en/jobs/365331/postdoc-on-decision-trees-for-control-and-monitoring-of-stochastic-systems/apply/

## About the Role

* 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++).

## Description

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., 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.