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academia and industry. Requirements The following qualifications are required: Solid knowledge in mathematics and statistics, in areas such as linear algebra, probability theory, machine learning, high
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in computer science, engineering, data sciences, applied mathematics, machine learning, or another related field; or Have completed at least 240 credits in higher education, with at least 60 credits at
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This is a broad call for five fully-funded PhD positions in computer science and engineering to work on machine learning, autonomous systems, software engineering, formal methods, and network
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given to the ability to assimilate third-cycle courses and study programmes at a higher education. The applicant should have documented knowledge in energy systems and machine learning technologies
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dynamics simulation and controls toolbox fascinating? The research of the PhD student will touch upon various topics multi-body dynamics, optimal control theory, machine learning and robotics and artificial
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diagnosis (biostatistics, machine/deep learning), ii) Investigating causal processes and disease mechanisms (causal inference and pathobiology). iii) Integrating knowledge of clinical implementation channels
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, implementation of methods in computer codes, use of state-of-the-art high-performance computers in Sweden and in Europe, application of machine-learning and AI techniques, and collaborations with experimental
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interdisciplinary project. The project concerns algorithm design, implementations of algorithms, and simulated and biological data analysis. The student is expected to learn a bit of relevant molecular biology to
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. We carry out research in the areas of electronic systems, machine learning and cyber-physical systems. The Electronics Systems group focuses on electrodynamic simulation and modeling. The research and
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) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from