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:
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.
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:
Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.
The Technical University of Munich (TUM) combines top-class facilities for cutting-edge research with unique learning opportunities for students. The university thinks and acts with an entrepreneurial spirit. Its aim: to create lasting value for society. All this combines to make it one of Europe’s leading universities. TUM's alumni include 17 Nobel laureates and 18 Leibniz Prize winners. In 2019, TUM secured the title "University of Excellence" for the third time in succession under the German Universities Excellence Initiative, winning every round since the excellence competition's inception in 2006.
Do you recognize yourself in this profile and would you like to know more about the IRIS cluster at TU/e and the Chair of Cyber-Physical Systems in Production Engineering at TUM? Please, visit https://iris.win.tue.nl and https://rtsl.cps.mw.tum.de.
For questions regarding this position, you can send an email to dr. Geoffrey Nelissen (g.r.r.j.p.nelissen[at]tue.nl) with email subject “PhD on time-predictable high-performance computing”.
Are you inspired and would like to know more about working at TU/e? Please visit our career page.
We invite you to submit a complete application by using the apply button. The application should include a:
We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.