Postdoctoral Researcher in Probabilistic Machine Learning

Job summary

This position is for a postdoctoral researcher focused on probabilistic machine learning and active inference for sequential decision-making under uncertainty. The role is part of a research project aimed at developing robust methodologies for dynamic pricing and other real-world applications.

Qualifications

• PhD degree (or equivalent) in machine learning, signal processing, data science, or a related field.
• Proven expertise in probabilistic machine learning and deep learning.
• Strong analytical and mathematical problem-solving abilities.
• Proficient programming skills in Python and experience with modern machine learning frameworks such as PyTorch and JaX.
• Experience working in HPC/GPU environments.
• A record of publications and presentations in reputable journals and conferences.

Responsibilities

• Conduct independent and collaborative research on probabilistic machine learning and active inference.
• Develop and implement novel models and algorithms, evaluating them through realistic use cases.
• Disseminate research findings through publications and presentations at international conferences.
• Participate in scientific collaborations and contribute to the academic environment, including limited teaching and supervision.

Skills

• Strong programming skills in Python.
• Familiarity with modern machine learning frameworks (e.g., PyTorch, JaX).
• Experience in high-performance computing and GPU environments.

Education

• A PhD degree (or equivalent) in a relevant field is required.

Tools

• Python, PyTorch, JaX, and other modern machine learning frameworks.