Postdoctoral Researcher in Machine Learning: 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. This position offers a collaborative research environment focusing on both theoretical and real-world applications.
Postdoctoral Researcher in Trustworthy Machine Learning
Designation
Postdoctoral Researcher
Research Area
Trustworthy Machine Learning
Location
Zurich, Switzerland
Eligibility/Qualification
- Degrees: Bachelor’s, Master’s, and PhD in Computer Science, Statistics, Mathematics, Electrical Engineering, or a related field.
- Background:
- For Theory Profile: Theoretical statistics, learning theory, probability theory, and optimization theory.
- For Experimental Profile: Deep knowledge of application domain, sufficient mathematical maturity for proof-writing, and exposure to learning theory/theoretical statistics.
- Publications: Proven research record with multiple first-authored publications in relevant peer-reviewed venues.
- Teamwork: Collaborative mindset and ability to work effectively in a multidisciplinary team.
Job Description
We are looking for candidates with expertise in one of the following profiles:
- Mathematical Foundations of Trustworthy ML:
- Robust distributional generalization, transfer learning, causality, multi-objective settings, and alignment.
- Concepts in statistical learning theory, optimization, and robustness.
- Grounding AI/ML concepts in social sciences (philosophy, psychology, law).
- Real-World Impact:
- Addressing scientific or engineering problems using proprietary/real data, focusing on distributional generalization, multi-objective trade-offs, causality, privacy, or interpretability.
- LLM adaptive evaluation and post-training with proof-of-concept mathematical proofs.
The project scope will be tailored to candidate strengths and expertise, with considerable freedom in selecting specific research problems while collaborating with SML team members and mentoring Bachelor’s and Master’s theses.
How to Apply
Interested candidates are encouraged to submit their applications online, including:
- CV
- Two references
- Research statement
- A short note on why they would like to join our group
Note: Applications via email or postal services will not be considered.
For inquiries regarding the position, please contact Prof. Fanny Yang at fan.yang@inf.ethz.ch.
Last Date to Apply
Applications are accepted on a rolling basis. Candidates are encouraged to apply as soon as possible.
