Arbetsbeskrivning

Join us to develop physics-guided, data-driven foundation models for multivariate time-series in safety-critical systems, with a primary focus on automotive applications. You will design predictive and generative models, scale training on real data, and validate in simulation and with industry partners to advance safe, reliable automation. About us  The Department of Computer Science and Engineering, a joint department of Chalmers and the University of Gothenburg. Our internationally visible research, strong industry links and diverse environment create a collaborative setting where ideas grow into real impact.  At the Division of Computing Science, we advance secure and trustworthy software and systems, spanning foundations, programming languages, tools and practical methods that help shape dependable digital infrastructures.  This project is a collaboration between AIXLab@Chalmers and Volvo Group. You will join us at AIXLab, where we focus on developing usable AI solutions that are applicable in real-world settings. You will work closely with engineers and researchers at Volvo Group, with direct access to industrial datasets, simulation environments, and real validation workflows. About the research project   This project advances physics-aware foundation models for time-series data. Here, foundation models refer to reusable, pretrained time-series models that can be adapted across vehicles, driving conditions, and tasks. The primary use case is automotive: predicting vehicle behavior, simulating rare safety-critical scenarios and generating test cases to strengthen validation and reduce physical trials. The techniques are designed to transfer to other safety-critical domains such as healthcare.  Concretely, the research will focus on multivariate vehicle time series such as CAN signals, sensor streams, and simulated state trajectories, with models that integrate physical structure (e.g. dynamics, constraints, conservation laws) into large neural architectures. Physics guidance may include explicit system constraints, inductive biases in model architectures, hybrid simulation learning loops, or loss formulations that encode physical consistency. The work combines forecasting, representation learning, and scenario generation under safety and reliability constraints. The results will support safer automation, fewer failure modes, more efficient testing, and lower energy use. Who we are looking for We are particularly interested in candidates who enjoy working at the intersection of theory, data, and real-world systems, and who are comfortable with imperfect, noisy, and safety-constrained data. The following requirements are mandatory: • To qualify as a Doctoral student, you must have a Master's degree (masterexamen) of 120 credits or a Master’s degree (magisterexamen) of 60 credits* in Computer Science, Electrical engineering, or equivalent • Strong written and verbal communication skills in English • Strong machine learning fundamentals (probability, statistics, optimization) and strong interest in time-series modeling and physics-guided machine learning • Proficiency in Python and modern deep learning frameworks (e.g., PyTorch) • Strong engineering maturity, including experience with large-scale GPU or cluster-based training, reproducible experiment pipelines, versioned datasets, and systematic evaluation • Ability to formulate research questions, run empirical studies at scale *for students with an education earned outside of Sweden, a 4-year Bachelor’s degree is accepted.  The following experience will strengthen your application:  • Physics-informed machine learning • Background in foundation models for time-series (forecasting, representation learning) • Exposure to safety-critical systems, scenario generation, or test coverage for edge cases • Academic research and publications What you will do • Take advanced courses within the Graduate school of Computer Science and Engineering • Develop your own scientific concepts and communicate the results of your research verbally and in writing • By the end of the PhD, the candidate is expected to have developed reusable modeling frameworks for safety-critical time-series data. The work should result in publications in top-tier machine learning or applied AI venues and contributions that influence industrial validation pipelines Contract terms   • The Doctoral student positions are fully funded from start • The position is limited to four years, with the possibility to teach up to 20%, which extends the position to five years • A starting salary of 34,550 SEK per month (valid from May 25, 2025) • Doctoral studies require physical presence throughout the entire study period. A valid residence permit must be presented by the study start date; otherwise the admission may be withdrawn What we offer  Chalmers is dedicated to improving gender balance and actively works with equality projects, such as the GENIE Initiative for gender equality and excellence. We celebrate diversity and consider equality and inclusion as fundamental aspects of all our activities.  If Swedish is not your native language, Chalmers offers Swedish courses to help you settle in. Find more general information about working at Chalmers, benefits  for employees, Sweden’s generous parental leave, subsidized day care, free schools, healthcare etc at Move To Gothenburg and doctoral studies at Chalmers here.   Application procedure The application should be written in English and attached as PDF-files.  CV  Personal letter  • A brief introduction about yourself • A brief motivation as to why you are interested in this position Bachelor’s and, if available, master’s thesis together with the transcripts.  Use the button at the foot of the page to reach the application form.   Please note: The applicant is responsible for ensuring that the application is complete. Incomplete applications and applications sent by email will not be considered. Contact details to references will be requested after the interview.  We welcome your application no later than March 3rd 2026 For questions, please contact:   Yinan Yu (about the research project) [email protected] Carl-Johan Seger (about the application process) [email protected] We look forward to your application!  Chalmers declines to consider all offers of further announcement publishing or other types of support for the recruiting process in connection with this position.