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 two profiles aligned with our lab’s main research branches:
1. Mathematical Foundations of Trustworthy ML
- (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)
2. Real-world Impact
- Real-world problems in scientific or engineering domains using proprietary/real data (beyond public benchmarks), where challenges like distributional generalization, multi-objective trade-offs, causality, privacy, or interpretability are relevant
- LLM adaptive evaluation and post-training, with clean mathematical proofs for proof-of-concept
Profile
The exact project scope will be tailored to your strengths and expertise. You'll have considerable freedom to select 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 adequate exposure to learning theory/theoretical statistics to comprehend the theoretical papers in our lab.
- Proven research record with multiple first-authored publications at:
- For the theory profile: 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 ML venues listed above.
- Teamwork: a collaborative mindset and the ability to work effectively in a multidisciplinary team. You should also align with our group values.
Workplace
Our lab emphasizes personal growth in core leadership competencies, and we expect you to actively develop along these dimensions. We provide an inspiring and collaborative research environment to support your involvement in an ambitious research agenda.
Our team consists of a dynamic and international group of researchers who share 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.
Curious? So are we.
We look forward to receiving your online application using the form below. Please ensure that your application includes:
- CV
- 2 references
- Research statement
- Short note on why you'd like to join our group
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.