Postdoc Position in Quantized Reinforcement Learning

Required Qualifications

• Education: A PhD in Computer Science, Mathematics, Statistics, or Theoretical Physics at the time of employment.
• Publication Track Record: At least two first-author research papers at flagship venues of core machine learning research (e.g., NeurIPS, ICML, ICLR, AISTATS).
• Theoretical Rigor: A deep understanding of statistical learning theory and reinforcement learning foundations, with the ability to conduct performance, convergence, or regret bound analysis (e.g., optimistic posterior sampling) in discrete or latent non-stationary environments.
• Implementation Expertise: Outstanding scientific programming skills (Python, PyTorch/JAX) with a proven track record of developing, debugging, and scaling deep generative models, discrete codebook world models, or complex RL pipelines. Clean public repositories or released source code from past publications is a strong plus.
• Algorithmic Breadth: Familiarity with probabilistic machine learning, evidential learning, discrete variational autoencoders (VQ-VAEs), transformers, or model-based RL is highly desirable.
• Communication: Excellent spoken and written communication skills in English.