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

Academic Positions; Schweiz; Presencial; CHF 45.000 - 55.000; Jornada completa

Descripción de la vacante

The Rhine AI group of Prof. I. Bogunovic at the University of Basel invites applications for a PhD position funded by the SNSF project Foundations of Post-Training for Reasoning in Diffusion Large Language Models.

The role focuses on post-training of large diffusion-based language models to improve reasoning capabilities; you will develop RL methods and explore reasoning-aware generation strategies. The position starts January 2027 (or later) and lasts 4 years.

Formación

• Master's degree in computer science, mathematics, physics, or a related field.
• Exceptional academic record and solid mathematical foundation.
• Strong coding and implementation skills; ability to run large-scale experiments.
• Experience with LLMs, reinforcement learning, or generative modeling is a plus.

Responsabilidades

• Conduct research in machine learning and artificial intelligence.
• Write research papers and articles; present results at international conferences.
• Interact with national and international teams of AI and ML experts.

Conocimientos

Machine learning

Reinforcement learning

Coding and implementation

Educación

Master's degree (CS/Math/Physics)

Descripción del empleo

Organisation/Company University of Basel Research Field Computer science » Other Researcher Profile First Stage Researcher (R1) Application Deadline 2 Dec 2026 - 22:59 (UTC) Country Switzerland Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

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 PhD 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. The duration of a PhD in Switzerland is 4 years.

• Conduct research in machine learning and artificial intelligence
• 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

Applicants must possess a Master's degree in computer science, mathematics, physics, or a related field. 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. Research experience is advantageous but not mandatory. 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.

The position will be based in Basel, Switzerland.

• Close supervision 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