Research Associate/Fellow AI Safety Assurance

Department
This role will give you the opportunity to advance state-of-art and empirical research in safe Artificial Intelligence (AI) and Autonomous Systems (AS) in a range of application domains including autonomous driving and healthcare. You will be key to establishing a new Centre, building on the work of the Assuring Autonomy International Programme (AAIP), which pioneered approaches to assuring autonomous systems and their machine learning (ML) components.

Role
You will be researching methods and techniques for developing and analysing safety requirements for ML components (used in safety-critical applications). You will investigate the relationships between safety requirements and other types of requirements such as performance, robustness and explainability requirements and consider trade offs.

You will study how machine learning requirements could be systematically derived from safety analysis and updated and validated through-life. You will develop approaches for determining and validating appropriate ML safety metrics or qualitative indicators, considering different uncertainties in the wider system and its constantly changing environment.

This is an inherently interdisciplinary research area and will require you to work closely and effectively with other team members and be able to explain your research clearly and precisely. You will evaluate your research through in-depth case studies, requiring close collaboration with industrial and regulatory partners.

Skills, Experience & Qualification needed

• First degree in Computer Science, Engineering, or cognate discipline.
• PhD in Computer Science, Systems Engineering, Human-Computer Interaction or Human Factors, or equivalent experience.
• Knowledge of safety requirements and specification techniques.
• Knowledge of safety analysis or safety cases.
• Knowledge of machine learning or data science.
• Ability to work as part of a diverse and multidisciplinary team.
• Competency in system safety processes and methods.
• Experience of researching safety-critical systems (analysis, assurance, human factors or safety management).

Interview date: to be confirmed.

For informal enquiries: please contact Professor Ibrahim Habli on [email protected] .

The University strives to be diverse and inclusive – a place where we can ALL be ourselves.

We particularly encourage applications from people who identify as Black, Asian or from a Minority Ethnic background, who are underrepresented at the University.

We offer family friendly, flexible working arrangements, with forums and inclusive facilities to support our staff. #EqualityatYork