Postdoctoral Researcher Positions: Advanced AI Techniques for Complex System Optimization
80%-100%, Fixed-Term
Our Sinergia consortium unites expertise at the University of Zurich (Mathematical Modeling & Machine Learning) and ETH Zurich (Department of Mathematics) to advance data-driven decision support in large-scale, real-world systems under the SNSF project—From Single-Disease Research to Informed Machine Learning. We develop innovative methods that combine reinforcement learning (RL) and large language models (LLMs) to optimize processes, automate design choices, and reason about constraints under uncertainty. We seek to appoint up to two post-doctoral researchers, each specializing in one of the complementary tracks outlined below. The precise application domain will be determined in consultation 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 to enhance 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 integrate structured reasoning tools (e.g., retrieval-augmented generation, knowledge graphs, constraint parsers).
Build workflows for transparent explanation and evaluation of model decisions.
Maintain and contribute to open-source libraries that support reproducible research in the project.
Both tracks will involve co-supervision of doctoral students, publication in leading academic venues, and contributions 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.
Documented hands-on experience applying RL and/or LLMs to real-world problems (e.g., publications, deployed systems, or open-source artifacts).
Excellent Python skills and familiarity with modern ML stacks (e.g., PyTorch, JAX, Hugging Face).
Adept at thriving in an interdisciplinary environment and communicating 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 vibrant, international research environment equipped with state-of-the-art computing resources and a family-friendly workplace in Zurich, offering competitive Swiss salaries and an excellent quality of life.
We Offer
Fully funded positions (SNSF postdoctoral scale, approximately CHF 100k/year).
Mentoring by Nicola Serra (Mathematical Modeling & ML, UZH) and Alessio Figalli (Mathematics, ETHZ), alongside the broader Sinergia team.
Close collaboration with partners in medicine, economics, and computer science.
A generous travel and training budget, as well as support for industry or clinical secondments.
Curious? So Are We.
We look forward to your response. Apply online using the form below. Please note that only applications matching the job profile will be considered.