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), enabling affordable and durable long-duration energy storage. The approach is to use hierarchical structures, i.e. complex material layers that can be optimized to specific battery chemistries and flow
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on III-nitride devices and circuits for both high frequency and power applications. We will explore new concepts in III-nitride semiconductor material and device processing to optimize different important
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), enabling affordable and durable long-duration energy storage. The approach is to use hierarchical structures, i.e. complex material layers that can be optimized to specific battery chemistries and flow
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systems. This PhD project, part of a national initiative, aims to use AI to design and optimize thermal interface materials (TIMs). It combines machine learning, materials informatics, and experiments
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Recognised Researcher (R2) Country Sweden Application Deadline 29 Sep 2025 - 22:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not
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Familiarity with optimization algorithms and design of experiments methodologies Experience with high-performance computing environments Track record of publications in peer-reviewed journals Previous
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Application Deadline 19 Sep 2025 - 22:00 (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 Reference Number 304--1
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23 Aug 2025 Job Information Organisation/Company Chalmers University of Technology Research Field Physics » Computational physics Researcher Profile Recognised Researcher (R2) Country Sweden
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environments ensure safe operations Our activities are primarily related to the development and application of numerical modelling (e.g. CFD, FEA, FSI, optimization, ML), but we are also involved in experiments
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fundamental questions about the particles and forces governing our Universe to energy-related research. The methods of our investigations are also diverse and complementary, and range from theory and computer