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PhD Student in Pervasive and Mobile Computing Skelleftea Reference number 4702-2026 Are you ready to take the next step in your academic journey? As a doctoral student with us, you will work closely
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genomics, proteomics, or metabolomics. Experience with relevant machine-learning frameworks, such as PyTorch, and high-performance computing (HPC) environments. Knowledge of molecular and cellular biology
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training and resources in computational science, including high-performance computing (HPC) clusters. For more information about the DDLS program, see here. The employment In connection with your admission
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experience in several of the following areas: Open source CFD, such as OpenFOAM Machine learning, deep learning, or reinforcement learning Scientific programming in Python and/or C++ High-performance
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extensive training and resources in computational science, including high-performance computing (HPC) clusters. For more information about the DDLS program, see here. The employment In connection with
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integrative multi-omics data, such as genomics, proteomics, or metabolomics. Experience with relevant machine-learning frameworks, such as PyTorch, and high-performance computing (HPC) environments
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sensitive data, such as medical diagnosis. The focus of this PhD project is to develop methods for reducing the dataset size without significantly affecting the performance of a model trained on that data
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and in high demand. Specifically there is a gap between a high-level goal like “cut carbon now” and the low-level controls of a site’s cooling, batteries, and compute hardware. In this project you