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 team. The position is available immediately, with an initial appointment for 1 year, renewable up to 3 years.
Job Description
We are looking for candidates with one of the two profiles below, aligned with the main research branches of our lab.
- 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):
- Addressing real-world problems in scientific or engineering domains using proprietary/real data (beyond public benchmarks), focusing on challenges like distributional generalization, multi-objective trade-offs, causality, privacy, or interpretability
- Large Language Model (LLM) adaptive evaluation and post-training, including clean mathematical proofs for proof-of-concept
Profile
The exact project scope will be tailored to your strengths and expertise. You will have considerable freedom to choose specific research problems, as long as you collaborate closely with SML team members and help mentor 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 follow the theoretical papers in our lab.
- Proven Research Record:
- For the theory profile: multiple first-authored publications at respected venues such as ICML, ICLR, NeurIPS, COLT, AISTATS, or similar peer-reviewed venues and journals like JMLR, Annals of Statistics/Probability, JASA, etc.
- For the experimental profile: publications in top journals in your application domain and at least one paper in ML venues listed above.
- Teamwork: A collaborative mindset and the ability to work effectively in a multidisciplinary team while aligning with our group values.
Workplace
Our lab emphasizes personal growth in core leadership competencies, and we expect you to actively develop along these dimensions.
Environment and Resources: We offer an inspiring and collaborative research environment to support your immersion into an ambitious research agenda. Our dynamic and international team shares a common vision to contribute to top-level academic research on trustworthy machine learning. You will 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 at ETH Zurich.
Interested? Apply Online!
We look forward to receiving your online application using the form below. Please note that 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.