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

Universität Basel; Basel; Presencial; CHF 110.000 - 140.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Close collaboration with national/in t

Travel to international conferences

Excellent salary and benefits

Descripción de la vacante

The Rhine AI group at the University of Basel is inviting applications for a Postdoc position funded by the SNSF project Foundations of Post-Training for Reasoning in Diffusion Large Language Models. You will develop reinforcement learning methods for post-training large diffusion-based language models and help build benchmarks for reasoning across domains.

The position is available starting January 2027 or later, with opportunities to collaborate with national and international AI researchers

Formación

• PhD in machine learning or related field with strong publication record.
• Excellent mathematical foundations and coding skills for large-scale experiments.
• Experience with LLMs, reinforcement learning, or generative modeling is a plus.

Responsabilidades

• Conduct research in machine learning and artificial intelligence.
• Lead research directions and mentor PhD students.
• Write research papers and present results at leading international conferences.

Conocimientos

Python

ML research

Research experience

English communication

Educación

PhD in machine learning

Herramientas

PyTorch

TensorFlow

Descripción del empleo

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 position

• 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

• 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