Postdoc Position in Post-Training of Diffusion Language Models (Prof. I. Bogunovic)

Summary: The Postdoc will conduct research on post-training large diffusion-based language models to improve reasoning capabilities. Responsibilities include developing reinforcement learning methods, exploring reasoning-aware generation strategies, and building evaluation benchmarks. The role involves mentoring PhD students...

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The research group Rhine AI of Prof. I. Bogunovic at the Department of Mathematics and Computer Science at the University of Basel is inviting applications for a Postdoc position funded by the Swiss National Science Foundation (SNSF) project "Foundations of Post-Training for Reasoning in Diffusion Large Language Models." The position focuses on post-training large diffusion-based language models (dLLMs), an emerging alternative to standard autoregressive LLMs, to improve their reasoning capabilities. The successful candidate will develop reinforcement learning methods for post-training large dLLMs, explore reasoning-aware generation strategies such as adaptive unmasking and credit assignment, and help build benchmarks for evaluating reasoning across domains, including mathematical problem solving. The ideal candidate has a strong background in machine learning and is excited about large-scale experimentation with modern AI systems, supported by principled analysis where it helps. This work will push the boundaries of what modern AI systems can reliably reason about.

The position is available starting January 2027 or later.

Your assignments

Your tasks include:

• Conduct research in machine learning and artificial intelligence
• Take a leading role in the project's research directions and help mentor PhD students
• Write research papers and articles, and present results at leading international conferences. We publish in venues such as ICML, NeurIPS, and ICLR
• Interact with national and international teams of AI and machine learning experts

Your profile

Applicants must hold a PhD in machine learning or a related field, with a strong publication record at leading machine learning venues. Candidates should have an exceptional academic record and a solid mathematical foundation. They are also expected to have strong coding and implementation skills, with the ability to run large-scale experiments. Experience with LLMs, reinforcement learning, or generative modeling is a plus.

Excellent communication skills and fluency in English (spoken and written) are required.

We also seek to increase the number of women and other groups in areas where they are underrepresented and therefore explicitly encourage such groups to apply.

We offer you

The position will be based in Basel, Switzerland. In addition, we offer:

• Close collaboration and mentoring, working with and access to national (EPFL and ETH among others) and international collaborators
• A diverse range of tasks in a dynamic environment
• The possibility of traveling to international conferences to meet distinguished scientists
• Excellent salary, benefits, and working conditions

Application / Contact

If interested, please send us an application as described below. Your application should include two PDF files containing the following:

• Short motivation statement (maximum 150 words) explaining your interest in the position and why your background is a good fit
• Curriculum vitae, including a list of publications
• Contact information (no direct recommendation letters) for people who can recommend you
• Your PhD thesis or up to two representative publications

You are encouraged to apply before November 20, 2026. However, there is no deadline, and the position will remain open until a suitable candidate is found.