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evolutionary analysis. A central component of the research will be to develop machine learning and deep learning methods trained on coding sequences and protein structure to extract patterns in data and to draw
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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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to intravital microscopy), electrophysiology, respirometry, microfluidics, organoid cultures, bioprinting, and excellent opportunities to work with various in vivo models. Infrastructure and expertise in various
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sustainable design, product development and environmental assessment will conduct case studies to integrate user behaviour into the early design of dishwashers, washing machines, refrigerators and ovens
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on developing methods for the verification and validation of systems that embed machine learning or generative models, addressing challenges such as non-determinism, data drift, and explainability. The project
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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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in vivo models. Infrastructure and expertise in various so-called omics technologies, single-cell biology, bioinformatics, drug development, and more are available locally through the SciLifeLab
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Parkinson. We use in vitro biophysical analysis to characterise protein aggregates and their formation in combination with advanced live cell fluorescence imaging and cell model development to study protein
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Injection Systems (CIS) — natural protein machines used by bacteria to deliver molecular cargo. The group's mission is to understand the structure, function, and application of CIS for use in both
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data analysis, non-Gaussian modeling, spatial and temporal stochastic modeling, Bayesian methods, or modern machine learning. This expertise should be supported by publications in top-tier journals