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experience in AI/machine learning. This project is funded by Vinnova under the FFI (Strategic Vehicle Research and Innovation) programme and is carried out in collaboration with Autoliv. Supervision
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on models for latent variables and their connections to modern machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The
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discovered. The successful candidate is expected to investigate the use of advanced machine learning techniques to enhance both the analysis and mitigation of fault attacks. The insights gained will be
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simulations (QM/MM and atomistic molecular dynamics simulations, free energy methods, and machine learning). Candidates with either computational and/or experimental expertise are encouraged to apply
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theory, machine learning, optimization, and decision-making. The project seeks to develop mathematical foundations and computational methods for the analysis, learning, and coordination of complex
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engineering systems through a model-driven approach, i.e. a combination of simulation- and data-driven methods and tools with data analysis and machine learning as an important part. The work builds on
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evolutionary transitions, machine learning, and genomics to decode pollen evolution and unravel sexual selection mechanisms shared across kingdoms” led by Prof. Tanja Slotte in close collaboration
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fluid dynamics, initially focusing on quantum-inspired techniques such as tensor networks and tensor-network-based machine learning. You will then investigate how these methods can be reformulated and
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flow and transport, fractured media, uncertainty quantification, statistical methods, or machine learning is considered a merit. Rules governing PhD students are set out in the Higher Education
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Computational Biology. Description of the subject area Computational biology comprises the development and application of computational, statistical, and machine-learning methods for analysing