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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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to which extent machine learning methods can help with these tasks, e.g. object reconstruction and signal vs background discrimination. This will become more of a focus later in the project. Beyond
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computational focus on innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. Main responsibilities Research
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criteria for the specified third cycle studies. Specific knowledge in machine learning, data analytics, sector-coupling and Mixed-Integer Linear Programming (MILP) is a merit. In addition to the above, there
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on machine learning, artificial intelligence, or other computational techniques. Main responsibilities Research and in addition some teaching and supervision. Qualification requirements In order to qualify
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dynamics, targeting large-scale systems equipped with GPUs and other accelerators. Key research topics include mixed-precision numerical methods, integrating machine learning into computational workflows
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advising on methods and systems, assessing quality and properties of data, assisting with resource allocation proposals, machine learning workflows, dataset curation, organization, and sharing, data
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interdisciplinary approach encourages contributions to related projects, including applications of machine learning to autoimmune disease and non-invasive diagnostics using cell-free nucleic acids. Duties Develop and
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). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction solvers. Computational aeroacoustics. Swedish is not required
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population registries and biobanks. The applicant is expected to have a strong computational focus on innovative development and application of novel data-driven methods relying on machine learning