Postdoctoral Researcher in Trustworthy Machine Learning 100%, Zurich, fixed-term The SML group at the Institute of Machine Learning is looking for 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 our lab’s main research branches. 1. 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) 2. 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), 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 pick the specific research problems as long as you will 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: 1) For the theory profile: theoretical statistics, learning theory, probability theory, and optimization theory. 2) 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 with multiple first-authored publications at: 1) 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. 2) 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 We offer Our lab emphasizes personal growth in core leadership competencies, and we expect you to actively develop along these dimensions. Environment and resources: We promise an inspiring, collaborative research environment to support your immersion into 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. Here are our group values. 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 with the following documents (note that applications without at least two references will not be considered): CV 2 references Research statement Short note on why you'd like to join our group Applications via email or postal services will not be considered. For any questions regarding the position, please contact Prof. Fanny Yang at fan.yang@inf.ethz.ch. If you apply for this position please say you saw it on Computeroxy

Postdoctoral Researcher in Trustworthy Machine Learning

100%, Zurich, fixed-term

The SML group at the Institute of Machine Learning is looking for 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 our lab’s main research branches.

1. 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)

2. 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), 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 pick the specific research problems as long as you will 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: 1) For the theory profile: theoretical statistics, learning theory, probability theory, and optimization theory. 2) 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 with multiple first-authored publications at: 1) 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. 2) 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

We offer

Our lab emphasizes personal growth in core leadership competencies, and we expect you to actively develop along these dimensions.

Environment and resources: We promise an inspiring, collaborative research environment to support your immersion into 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. Here are our group values. 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 with the following documents (note that applications without at least two references will not be considered):