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of MSI advances our understanding of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as
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hampers our ability to establish causal relations between molecular alterations and disease phenotypes. In this PhD you will address this by developing a deep learning model of cancer. The PhD position
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machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid understanding of fluid dynamics and heat transfer, as
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or more of the following programming languages/environments: Unity/Unreal, C/C++, C#, Python Basic understanding of the artificial intelligence and machine learning fields. Place of employment: Karlskrona
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at the single-cell level, using tools from optimal transport, mathematical optimization, and machine learning. In addition to method development, the work includes applying and benchmarking algorithms on both
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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
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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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biology. The applicant should also have an interest in learning, or previous experience in, computer programming, particularly using languages such as Python. The ideal candidate is driven and a creative
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of the PhD student will touch upon various topics multi-body dynamics, optimal control theory, machine learning and robotics and artificial intelligence in general. The focus is broadly upon the development