Postdoctoral Researcher Positions in Advanced AI Techniques
80%-100%, fixed-term
Our Sinergia consortium combines expertise from the University of Zurich (Mathematical Modeling & Machine Learning) and ETH Zurich (Department of Mathematics) to advance data-driven decision-making in large-scale, real-world systems under the SNSF project "From Single-Disease Research to Informed Machine Learning." Our focus is on developing methods that integrate reinforcement learning (RL) and large language models (LLMs) to optimize processes, automate design choices, and reason about constraints under uncertainty. We are seeking to appoint **up to two post-doctoral researchers**, each specializing in one of the complementary tracks detailed below. The specific application domain will be determined in collaboration with the successful candidates and our research partners in epidemiology and related fields.
Job Description
Track-A - RL / Optimization
Design, implement, and evaluate RL frameworks for complex, high-dimensional environments, utilizing simulation-based optimization and digital-twin testbeds.
Explore multi-agent or distributed training techniques for scaling optimization across interacting subsystems.
Collaborate with domain scientists to translate algorithms into prototype decision-support tools.
Track-B - LLM / Knowledge-Engineering
Develop and fine-tune LLM pipelines that incorporate structured reasoning tools (e.g., retrieval-augmented generation, knowledge graphs, constraint parsers).
Create workflows that ensure transparent explanation and evaluation of model decisions.
Maintain and contribute to open-source libraries that support reproducible research within the project.
Both tracks will co-supervise doctoral students, publish in leading venues, and contribute to open-source tooling.
Profile
Must Have (Both Tracks)
PhD (or equivalent) in computer science, applied mathematics, operations research, physics, statistics, or a related field.
A documented, hands-on track record of applying RL and/or LLMs to real-world problems (publications, deployed systems, or open-source artifacts).
Excellent Python skills and familiarity with modern ML stacks (e.g., PyTorch, JAX, Hugging Face).
Ability to thrive in an interdisciplinary environment and communicate complex ideas clearly.
Prior experience in healthcare, epidemiology, or medical applications of machine learning or statistics (e.g., causal inference) is not required.
Nice to Have - Track-A
Experience with simulation-based optimization or digital-twin frameworks.
Familiarity with multi-agent RL or distributed training.
Track record of open-source contributions to RL, LLM, or optimization libraries.
Workplace
We offer a fully funded position within a vibrant, international research environment with:
Competitive Salary: SNSF postdoctoral scale, approximately CHF 100k/year.
Mentorship: Guidance from experts Nicola Serra (Mathematical Modeling & ML, UZH) and Alessio Figalli (Mathematics, ETHZ), along with the broader Sinergia team.
Collaboration: Close cooperation with partners in medicine, economics, and computer science.
Resources: Access to state-of-the-art computing resources, a generous travel and training budget, and support for industry or clinical secondments.
Environment: A dynamic, family-friendly workplace in Zurich offering competitive Swiss salaries and an excellent quality of life.
Curious? So Are We.
Apply online using the form below. Only applications matching the job profile will be considered.