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in Python programming. Experience with machine learning methods, bioinformatics, and data science. Familiarity with generative AI tools for protein design and protein language models. Knowledge
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and documented background in machine learning, deep learning, data analysis and programming. Previous experience in research and knowledge in bioinformatics, biophysics, biochemistry, molecular biology
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Sciences division. This multidisciplinary team utilises a combination of machine learning and mechanistic modelling to derive models and scientific insights from data, which both support and enhance drug
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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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and computational modeling to understand complex biological processes. Experience in statistical modeling, machine learning, or analysis of spatial or high-dimensional biological data is considered
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of small cryptic plasmids in the development and spread of antibiotic resistance, and ii) Use machine learning tools to examine the complex interplay between bacterial hosts, various plasmids and resistance
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well as the clinical activities at the Karolinska University Hospital, unique access to international expertise in machine learning, state-of-the-art imaging, diverse patient cohorts, and relevant computational
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variability in risk factor susceptibility, treatment response, disease pathogenesis, and clinical diagnosis (biostatistics, machine/deep learning), ii) Investigating causal processes and disease mechanisms
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vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid
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research and methodological development to design and implement novel computational models and solutions. A solid theoretical background and hands-on experience in digital image processing and deep learning