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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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), signal processing, machine learning, computer vision, video processing After the qualification requirements, great emphasis will be placed on personal skills. Target degree: Doctoral degree Information
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spatial mass spectrometry. Experience with single-cell omics is also an advantage. Advanced biostatistics and machine learning, such as multivariate analysis, regularization, deep learning, or network
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to compare geophysical parameters with chemical and mineralogical analyses (e.g., XRD, SEM, geochemistry). AI and machine learning applications: Develop and apply AI methods to identify patterns and
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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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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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The Department of Ecology, Environment and Plant Sciences invites applications for postdoktoral fellow for the project “Harnessing evolutionary transitions, machine learning, and genomics to decode pollen
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interest in Artificial Intelligence and Machine Learning development, Proficiency in written and oral communication in English. Place of employment: Karlskrona. Employment level: 100%. Commencement: To be
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and documented background in machine learning, deep learning, data analysis and programming. Previous experience in research and knowledge in bioinformatics, biophysics, biochemistry, molecular biology
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Engineering and Autonomous Systems division . We offer advanced PhD courses where we extend the fundamentals in optimal control, machine learning, probability theory and similar. The research and learning