There is a widespread adoption of AI in most application domains, including in safety- and mission-critical systems. This raises new challenges as these systems must undergo certification processes to prove that they will not harm users, by-standers, the environment, other systems or lead to undesirable, unforeseen or dangerous situations. Such safety- and mission-critical systems must therefore meet a combination of safety, domain-specific, and high-performance requirements to execute complex and data-hungry AI-applications in a provably safe manner. This notably include requirements that the system's performance must be predictable and analyzable so as to provide worst-case guarantees that can be used as evidence during their certification.

A major roadblock to achieving time-predictable high-performance computing originates from the complexity of the modern execution platforms designed to meet the computational needs of AI-applications. On such platforms, the need to reduce power consumption while maximizing peak performance results in a high-degree of resource sharing (caches, DRAM, bus, I/Os) causing unpredictable interference, thus preventing guaranteeing that the AI-application's timing requirements will be met once deployed. This is an unprecedented challenge from a safety and real-time perspective.

We look for a PhD candidate who will work on developing a predictable execution platform for AI-oriented computing to achieve higher control over the system's predictability, enable its analyzability —hence providing required properties towards its certifiability—, with no performance degradation.

The project will work towards:

• developing an execution layer (acting as a middleware) to exploit the capabilities of modern computing platforms to enforce predictable behavior and to efficiently utilize the available computing power through dynamically orchestrating access to shared resources. 
• Developing modeling and timing analysis tools to produce evidences usable as safety arguments during certification.

The candidate will integrate with the Interconnected Resource-aware Intelligent Systems cluster at TU/e and the Chair of Cyber-Physical Systems in Production Engineering at TUM.

• You have a master's degree (or an equivalent university degree) in computer science or electrical engineering.
• You have a strong background in at least two of the following areas: real-time systems theory and/or development, operating systems, (co-)processor design/emulation, high-performance computing and networking..
• We are looking for a talented, motivated and enthusiastic researcher who has analytical skills, initiative and creativity.
• You are a naturally curious person who is eager to learn more and has a strong interest in research.
• You have a team spirit and the ability to work in an internationally oriented environment.
• You are fluent in spoken and written English (equivalent to at least 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 of Eindhoven. You will spend extended periods of time (for a total of at least 1/3 of your PhD) at TU Munich in Germany.

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 related to your research topic.
• 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.
• 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 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 meeting the correct requirements.