• Are you inspired by the prospect of shaping the future of autonomous driving?
• Are you fascinated by explainable artificial intelligence? 
• Are you eager to work on an interdisciplinary team that combines engineering and the humanities?
• Then apply for the PhD position on Explainable AI in Machine Vision for autonomous driving!

Job Description

Autonomous driving is a key application of artificial intelligence, and machine vision specifically. Although contemporary machine vision systems now routinely outperform their biological counterparts, they are far from perfect, especially when they are integrated with the complex action-selection systems that drive autonomous vehicles.

Methods from explainable AI (XAI) are increasingly used to evaluate and improve the performance of AI systems. Although the technical implementation of these methods is becoming increasingly routine, it remains unclear exactly how these methods can be used most effectively to ensure the safe, responsible, and transparent artificial intelligence. For example, although XAI methods can be used to precisely characterize an AI system's classification performance, it remains unclear how much error can and should be tolerated in this performance. Moreover, although other XAI methods can tell us which factors are actually considered when decisions are being made, it remains unclear which factors are permissible and which ones are not.

This PhD project is designed to identify systemize and evaluate XAI methods, and to identify best-practices for machine vision in the context of autonomous driving, while taking into account human factors and societal norms. To this end, it will be necessary to not only consider mathematical and technical details, but also relevant insights from e.g. psychology of human decision-making, regulation & standardization of explainability, and ethical principles of safety, transparency, privacy, and fairness.

More specifically, research tasks will include:

• Reviewing relevant literature from the social sciences and humanities on AI safety and explainability.
• Reviewing technical literature on machine learning and explainable AI.
• Developing a normative evaluation framework for the use of explainable AI in machine vision for autonomous driving.
• Collaborating on ongoing engineering projects that aim to implement XAI methods in machine vision for autonomous driving.

As this is an inherently interdisciplinary research project, the ideal candidate will combine technical expertise in machine learning and explainable AI (e.g. visualization techniques and feature-importance measures) with an ability to engage relevant issues in social science and humanities (in particular, norms of explainability and AI safety).

The candidate will be integrated in the Mobile Perception Systems (MPS) lab as well as the Philosophy & Ethics (P&E) group. They will be a member of the LTP ROBUST consortium funded by  NWO and NXP Semiconductors, and of the EAISI institute at TU/e.

• A master's degree (or an equivalent university degree) in computer science, cognitive science, or a related discipline.
• Demonstrable interest and experience in relevant social science and humanities fields such as philosophy, psychology, and/or governance.
• Demonstratable engineering and programming skills required for AI research (e.g. experience with deep learning frameworks like PyTorch or Tensorflow).
• A research-oriented attitude.
• Ability to work in an interdisciplinary team and interested in collaborating with industrial partners.
• Motivated to develop teaching skills and coach students.
• Fluent in spoken and written English (C1 level).

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. €2,770 max. €3,539).
• 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.