Postdoctoral Researcher in Trustworthy Machine Learning
100%, Zurich, fixed-term
The SML group at the Institute of Machine Learning is seeking highly motivated postdoctoral researchers with expertise in trustworthy machine learning to join our dynamic team. The position is available immediately, with an initial appointment for 1 year, renewable up to a total of 3 years.
Job Description
We are looking for candidates that fit one of the two profiles below, aligned with our lab's main research branches.
- Mathematical Foundations of Trustworthy ML: Working on topics such as:
- (Robust) distributional generalization, transfer learning, causality
- Multi-objective settings and alignment, RL theory
- Statistical learning theory, optimization (e.g., implicit bias)
- Robustness (broadly defined), privacy, memorization, unlearning, interpretability
- Grounding AI/ML concepts in the social sciences (philosophy, psychology, law)
- Real-World Impact: Working on topics including (but not limited to):
- Real-world problems in scientific or engineering domains using proprietary/real data (beyond public benchmarks), addressing challenges like distributional generalization, multi-objective trade-offs, causality, privacy, or interpretability
- LLM adaptive evaluation and post-training, featuring robust mathematical proofs for proof-of-concept
Profile
The exact project scope will be tailored to your strengths and expertise. You will have the freedom to choose specific research problems, as long as you collaborate closely with SML team members and assist in mentoring Bachelor’s and Master’s theses.
We are seeking candidates with the following background:
- Degrees: Bachelor’s, Master’s, and PhD in Computer Science, Statistics, Mathematics, Electrical Engineering, or a related field
- Solid Background:
- For the theory profile: theoretical statistics, learning theory, probability theory, and optimization theory.
- For the experimental profile: deep knowledge of your application domain, plus sufficient mathematical maturity to write clean proofs and enough exposure to learning theory/theoretical statistics to engage with the theoretical papers in our lab.
- Proven Research Record:
- For the theory profile: multiple first-authored publications in ICML, ICLR, NeurIPS, COLT, AISTATS, and similar peer-reviewed venues and/or journals such as JMLR, Annals of Statistics/Probability, JASA, etc.
- For the experimental profile: top journals in your application domain and at least one paper in the machine learning venues listed above.
- Teamwork: A collaborative mindset and the ability to work effectively in a multidisciplinary team. Alignment with our group values is essential.
Workplace
Our lab emphasizes personal growth in core leadership competencies, and we expect you to actively develop in these areas. You will find an inspiring, collaborative research environment that supports your engagement with challenging research agendas. Join a dynamic, international group of researchers who share a vision for contributing to top-level academic research in trustworthy machine learning. You will also have access to state-of-the-art computational resources and a broad network of global collaborators.
Compensation
We offer a competitive salary at the standard rate for ETH Zurich.
Curious? So Are We.
We look forward to receiving your online application using the form below. Only applications matching the job profile will be considered.
For any questions regarding the position, please contact Prof. Fanny Yang at fan.yang@inf.ethz.ch.
Work locationZürich, Switzerland