PhD on Analysis of Bayesian Intelligent Autonomous Systems

Short Description

Are you interested in Artificial Intelligence systems that learn from interactions with their environments? Have you wondered what the best way to collect data is for them? How much information they gain by observing specific data points? Then this PhD position might be for you. We are looking for someone that is interested in uncovering information-theoretic properties of intelligent systems that learn from their environment.



Job Description

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.

You will become a member of the Bayesian Intelligent Autonomous Systems laboratory (https://biaslab.github.io/), which is part of the Signal Processing Systems group at the Electrical Engineering department. We are a close team of over a dozen researchers that work on probabilistic models, inference algorithms, and signal processing / control system applications. We are known for our probabilistic programming toolbox RxInfer.jl (https://rxinfer.ml/) and our foundational perspective on Artificial Intelligence (https://youtu.be/2wnJ6E6rQsU?si=UYgNxU5LeFd1Nq6P).

Requirements:

• 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).

Salary Benefits:

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,872 max. €3,670).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates. 

38 hours per week

De Rondom 70