13 high-performance-computing uni jobs at Chalmers University of Technology in Sweden
Sort by
Refine Your Search
-
We are looking for a motivated doctoral student who wants to explore how generative AI can transform the design of advanced heat exchangers for future aircraft propulsion systems. About the position
-
2025 - 12:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within
-
Flagship is a research program funded by the European Commission. It brings together academic and industrial researchers to take graphene and related materials from academic laboratories to European society
-
Nanoscience employs more than 40 people performing world-class research on high speed electronic devices, circuits, and systems for a wide range of communication and sensing applications in the frequency range
-
motivated PhD candidates who want to enter a doctoral program at the forefront of science. Our PhD students develop abilities to plan, perform, critically review, and present their research. PhD studies
-
of AI and materials science, addressing real-world challenges in thermal management for high-performance computing and 6G infrastructure. We offer: (1) You will develop marketable skills in AI, ML, and
-
Application Deadline 10 Nov 2025 - 12:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
-
into molecular structure-property relationships, and obtain understanding of chemical reactions. We frequently collaborate with colleagues from synthetic organic chemistry, computational surface science and
-
research area Sustainable Built Environments deals with concepts, tools, and strategies to enhance the sustainability performance of construction materials, building products, road infrastructures, and
-
actions to evaluate, balancing safety and computational effort. You will compare deep learning–based methods and probabilistic machine learning approaches, and explore extensions to active reachability