47 phd-neural-computation-"University-of-Exeter" positions at Chalmers University of Technology
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cells. Reference number: 20250273 Application deadline: August 10, 2025 Project overview This 5-year PhD project aims to develop a flexible and general model that enables comparison between different
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for a PhD student focusing on future fuels and propulsion systems for the maritime sector. This is an opportunity for you to contribute to the development of techno-economic and environmental assessment
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Application Deadline 28 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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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
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(Master’s) level (or equivalent). By the start of enrollment in the doctoral program, the candidate must hold a Master’s degree, including a Master’s thesis equivalent to at least one term (30 ECTS credits
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PhD students in the Automation group, with the primary goal of qualifying for a future academic career. Contribute to research projects within discrete-event systems, supervisory control theory, and
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aimed at building a high-performance quantum computer based on superconducting circuits. Our team includes a dynamic mix of PhD students, postdocs, and senior researchers working collaboratively
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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the sustainability performance of the built environment. Job description We are looking for three student assistants to support the PhD project Measuring to Manage: A Methodological Framework for Quantifying Plastic