Not all data is equally useful. A major challenge in training artificially intelligent systems that learn from interactions with their environments (agents), is to acquire the most useful data points. For example, where should a robot look in order to pick up a cup? Active Inference is a framework for designing agents that balance information-seeking and goal-seeking behaviour. This PhD position will dive into the information-theoretic basis of this framework.

You will work with probabilistic machine learning methods, such as (variational) Bayesian inference and Active Inference, applied to signal processing and control systems. We are looking for someone that has experience with information theory, i.e., someone who is familiar with concepts such as entropy, mutual information and divergence measures. You will use this knowledge to derive insights into whether the data acquisition protocols for Active Inference agents can be improved.

Qualifications 

•  A master’s degree (or equivalent university degree) in Electrical Engineering, Mathematics, Computer Science or Physics.
• A curious and research-oriented attitude.
• Ability to work in an interdisciplinary team.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level