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that translate this data into meaningful indicators of battery health, performance, and lifetime. The work combines physics-based modelling with data-driven approaches, with the goal of enabling accurate and
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, or applied physics and electrical engineering, with at least 240 ECTS credits, of which at least 60 are cycle 2, with a specialization in computer vision or equivalent. Alternatively, you have acquired
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backgrounds include agricultural science, environmental science, geography, environmental engineering, physics, biology, engineering, or other related fields. We value motivation, curiosity, and the ability
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maximum of 20 per cent of full-time. Your qualifications You have a master’s degree in electrical engineering, engineering physics, computer science, applied mathematics or have completed courses with a
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other departmental duties, up to a maximum of 20% of full-time. Your qualifications You have graduated at Master’s level in biomedical engineering, biophysics, applied physics, materials science, organic
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, computer science, mathematics, statistics, physics, or a related area that is considered relevant for the research topic of the project, or completed courses with a minimum of 240 credits, at least 60 of which must
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work assignments A wide variety of physical phenomena like radio transmission, ultrasound, acoustics, or tsunami modelling involve the solution of partial differential equations (PDEs) that model wave
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level in machine learning, computer science, mathematics, statistics, physics, or a related area that is considered relevant for the research topic of the project, or completed courses with a minimum of
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes