PhD Candidate in Probabilistic Machine Learning (Remote)

Job summary

This position offers a fully funded PhD scholarship focused on probabilistic machine learning and decision-making under uncertainty, with a duration of three years. The research will explore active inference as a framework for developing robust methods for sequential decision-making, particularly in dynamic pricing applications.

Qualifications

• Strong foundation in probabilistic machine learning and deep learning
• High level of mathematical proficiency
• Experience in high-performance computing environments
• Proficient programming skills in Python and familiarity with modern machine learning frameworks such as PyTorch, JAX, TensorFlow
• A two-year master's degree (120 ECTS points) or equivalent academic qualification

Responsibilities

• Conduct independent and collaborative research on probabilistic machine learning and active inference
• Develop and evaluate novel models and algorithms using contemporary machine learning frameworks
• Investigate methodologies through practical use cases, including dynamic pricing
• Disseminate research findings through publications in reputable international journals and conferences
• Engage in scientific collaborations and contribute to the academic environment, including limited teaching and supervision activities

Education

Salary and appointment terms

• A two-year master's degree (120 ECTS points) or a similar degree with an equivalent academic level is required.
• The appointment is based on a collective agreement with the relevant professional associations, with a three-year employment period. The preferred start date is September 1, 2026, or as mutually agreed.