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experiments; supporting other group members with data analysis and interpretation from both simulations and experimental data; and use the developed framework to design new materials with optimised performance
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research team. Good knowledge and experience in heat and mass transfer is essential and proficiency in the use of Computational Fluid Dynamics will be considered an advantage. The student will benefit from
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or in an academic role. We will help you develop into a dynamic, confident and highly competent researcher with wider transferable skills (communication, project management and leadership) with
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of tomorrow and creating novel solutions to major global challenges. Our community is made up of 13 000 students, 400 professors and close to 4 500 other faculty and staff working on our dynamic campus in Espoo
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explore or optimise the flexible structures and manufacturing process of Litz wires. This studentship offers the opportunity for the PhD student to lead the development of innovative simulation tools
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical
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only for up to 4 years full-time or up to a maximum of 6 years if studying on a part-time (0.5 FTE) basis How to apply: Send a copy of your CV and a 300-word statement about why you are interested in
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, lack of transparency, safety assurance, and sustainability. You will work at the forefront of AI research, exploring formal and dynamic verification methods, explainable AI, and data space integration
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, usability, and insight into leakage dynamics across diverse constructions. Research Objectives The project is structured around three synergistic work packages: Descriptive Analytics: You will conduct a
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load emulation, surface tribology and lubricants, contact mechanics or dynamical phenomena. This is an opportunity to work within a world-class multidisciplinary team within the Engineering Systems