PhD Candidate in Probabilistic Machine Learning

Job summary

This position offers a fully funded PhD scholarship focused on probabilistic machine learning and decision-making under uncertainty, specifically through the lens of active inference for dynamic pricing. The scholarship lasts for three years and involves both independent and collaborative research.

Qualifications

• Strong foundation in probabilistic machine learning and deep learning
• Mathematical maturity
• Experience in high-performance computing environments
• Proficient programming skills in Python and modern machine learning frameworks (e.g., PyTorch, JAX, TensorFlow)
• A two-year master's degree (120 ECTS points) or equivalent academic qualification

Responsibilities

• Conduct research on probabilistic machine learning and active inference
• Develop and evaluate novel models and algorithms
• Implement methodologies using modern machine learning frameworks
• Investigate real-world applications, particularly in dynamic pricing
• Publish research findings in leading international journals and conferences
• Engage in scientific collaborations and contribute to academic activities, including limited teaching and supervision

Education

• A two-year master's degree (120 ECTS points) or equivalent

Tools

• Python, PyTorch, JAX, TensorFlow