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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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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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include the design and implementation of finite element multiscale models and machine learning algorithms, analyzing related experimental data, and collaborating with industrial collaborators to validate
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systems, and machine learning. While the initial focus of the position is on this project, we offer significant opportunity for the applicant to develop their own independent research trajectory in
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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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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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focuses on the creation of visual representations that create insights and clarification of complex data. This includes the interpretability and explainability of machine learning models
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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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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