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Computational Fluid Dynamics (CFD), fluid mechanics, and Artificial Intelligence (AI), with a particular focus on developing deep reinforcement learning methods for active flow control of hydraulic
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methods with a particular focus on deep learning and image analysis. The project relies on a close collaboration with researchers at the Department of Immunology, Genetics and Pathology (IGP) at Uppsala
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professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/ Your work assignments Machine learning, and in particular deep learning, requires
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develop deep-learning-based methods to build multiscale structural models of cell membranes, starting from erythrocyte and mitochondrial membranes. Building on our group’s work on AlphaFold-based
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. Knowledge of statistics, AI/ML, mathematical modeling is highly desirable. Knowledge of AML and treatment resistance as well as single-cell deep learning frameworks such as scVI are desirable. Personal
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requirement. Knowledge of statistics, AI/ML, mathematical modeling is highly desirable. Knowledge of AML and treatment resistance as well as single-cell deep learning frameworks such as scVI are desirable
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active role in the interdisciplinary research environment at the eHealth Institute and within Samverkan e-hälsa apply and further develop expertise in machine learning, deep learning, natural language
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computational component and will include advanced deep learning methods for image analysis and genomic data integration to identify molecular programs associated with different functional cell states
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. On the computational side, the doctoral student will apply and further develop state-of-the-art deep learning and reinforcement learning approaches for protein design. On the experimental side, the
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ability to communicate clearly Merits include: Knowledge of LLMs, deep learning, and Python programming Knowledge of power electronics Experience in modelling, simulation, and experimental work In an