PhD scholarship in AI-Driven Methods and Tools for Distributed Cyber-Physic at Danmarks Tekniske Universitet

Copenhagen, Capital, Denmark -

Full Time

Start Date

Immediate

Expiry Date

15 Sep, 26

Salary

0.0

Posted On

17 Jun, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Combinatorial Optimization, Formal Analysis, Reinforcement Learning, Large Language Models, Model-Based Systems Engineering, Algorithms, Data Structures, Software Tool Development, Cyber-Physical Systems, Real-Time Systems, Embedded Systems, Distributed Systems, Time-Sensitive Networking, Mixed-Criticality Applications, Programming

Industry

Research Services

Description

A PhD scholarship is available in the Embedded Systems Engineering section at DTU Compute. The position is part of AI4SDV, a Chips Joint Undertaking project on AI-assisted methods and tools for software-defined vehicle (SDV) engineering automation. AI4SDV studies how AI can assist design, configuration, analysis, validation, and verification along the SDV engineering life cycle. At DTU, we focus on AI-driven methods and tools for model-based systems engineering of distributed cyber-physical real-time systems. The PhD project will study methods that combine machine learning, combinatorial optimization, formal analysis, reinforcement learning, and large language models. We consider distributed architectures such as those found in vehicles, including Time-Sensitive Networking (TSN) for in-vehicle communication and mixed-criticality applications that share hardware, network, and software resources. Responsibilities and qualifications Your main task will be to develop AI-driven methods and prototype tools for analysis and configuration in model-based engineering workflows for distributed real-time systems. The exact PhD topic will be defined together with the supervisors. Relevant directions include: * AI-based methods that predict or approximate analysis results and then check the results with deterministic tools. * AI-assisted combinatorial optimization for configuration, mapping, scheduling, routing, or resource allocation problems. * LLM-based synthesis of models, constraints, test cases, and prototype code used by analysis and configuration tools. * Model-based workflows that connect requirements, architecture models, analysis artifacts, and generated configurations. * Experiments on vehicle-inspired distributed architectures using TSN and mixed-criticality applications. * More details on relevant research problems are available from Paul Pop's Google Scholar page [https://scholar.google.com/citations?hl=en&user=zQdQ2uIAAAAJ&view_op=list_works&sortby=pubdate] and Silviu Craciunas' Google Scholar page [https://scholar.google.com/citations?hl=de&user=Z0-uh2EAAAAJ&view_op=list_works&sortby=pubdate]. You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. We expect that you have a background in: * Algorithms and data structures. * Machine learning. * Programming and software tool development. * An interest in cyber-physical systems, real-time systems, embedded or distributed systems, optimization, or formal methods. Approval and Enrolment The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education [https://www.dtu.dk/english/Education/PhD/Rules]. Assessment The assessment of applicants will be based on academic qualifications, background in algorithms and machine learning, programming ability, motivation, and fit with the research area. The assessment will be carried out by an academic assessment committee. We offer DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility. Salary and appointment terms The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years. Starting date is 1 October 2026 (or according to mutual agreement). The position is full-time. You can read more about career paths at DTU here [http://www.dtu.dk/english/about/job-and-career/working-at-dtu/career-paths]. Further information Further information may be obtained from Prof. Paul Pop (paupo@dtu.dk) and Associate Prof. Silviu Craciunas (silcr@dtu.dk). You can read more about DTU Compute at www.compute.dtu.dk [http://www.compute.dtu.dk] and about Paul Pop [https://orbit.dtu.dk/en/persons/paul-pop/] and Silviu Craciunas [https://orbit.dtu.dk/en/persons/silviu-craciunas/]. If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark [https://www.dtu.dk/english/about/job-and-career/moving-to-denmark]. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar [https://www.dtu.dk/english/about/job-and-career/moving-to-denmark/before-arrival/phd-relocation-to-denmark]” for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU. Application procedure Your complete online application must be submitted no later than 16 August 2026 (23:59 Danish time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", fill out the online application form, and attach all your materials in English in one PDF file. The file must include: * A letter motivating the application (cover letter) * Curriculum vitae * Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale * An example text written by you, such as a thesis chapter, report, paper, or substantial technical documentation * An example code sample written by you, or a repository link with a short explanation of your contribution You may apply prior to obtaining your master's degree but cannot begin before having received it. Applications received after the deadline will not be considered. All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply. As DTU works with research in critical technology, which is subject to special rules for security and export control, open-source background checks may be conducted on qualified candidates for the position. Embedded Systems Engineering (ESE) [https://www.compute.dtu.dk/english/research/research-sections/ese] is one of the 10 research sections at DTU Compute. Our mission encompasses the creation of insights that allow the development of context-aware, distributed, and embedded cyber-physical systems, with a particular focus on Internet-of-Things (IoT) and the computing continuum eras. Our vision is to pioneer advancements in high-tech distributed and embedded systems technology, driving change, and contributing positively to society. We strive for a future where our research and innovations form the cornerstone of technological advancements, and we're excited to include more brilliant minds in our journey. See our publications [https://orbit.dtu.dk/en/organisations/embedded-systems-engineering/publications/] and projects [https://orbit.dtu.dk/en/organisations/embedded-systems-engineering/projects/] here [https://orbit.dtu.dk/en/organisations/embedded-systems-engineering]. DTU Compute DTU Compute – Department of Applied Mathematics and Computer Science – is an internationally recognised academic environment with over 400 employees and 10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and implementation of complex systems. We focus on ensuring that our research results contribute to creating a better society by supporting areas such as health, green transition, energy supply, and life science. We collaborate with universities, public and private organisations, and companies in Denmark and abroad, and through DTU’s startup ecosystem, we encourage innovation and entrepreneurship. We have a strong ethical, human, and sustainable approach that ensures integrity in our work. Therefore, we strive for and take responsibility for driving the democratisation of digital technologies, so that everyone has the opportunity to actively participate in the development, and we ensure a continued open, democratic, and inclusive society for the benefit of all. At DTU Compute, we value diversity, inclusion, and a flexible work-life balance. Read more about us at www.compute.dtu.dk [http://www.compute.dtu.dk]. DTU – For the benefit of society since 1829 DTU is one of Europe's leading elite technical universities. Through research and education at an international top level, we create solutions to the major societal challenges of our time and help secure Europe's global leadership in sustainable technological development. Since Hans Christian Ørsted founded DTU almost 200 years ago, our mission has remained the same: We develop and create value through the natural and technical sciences for the benefit of society. DTU has 13,800 students, 1,600 PhD students, and 6,500 employees. We work in an international environment and have an inclusive, stimulating, and informal work culture. DTU has campuses in all parts of Denmark and in Greenland and collaborates with the best universities around the world.

Responsibilities

Develop AI-driven methods and prototype tools for analysis and configuration within model-based engineering workflows for distributed real-time systems. The role involves studying the integration of machine learning and formal analysis for software-defined vehicle engineering automation.