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environments with minimal environmental impact. We are recognized nationally and internationally for our excellence in numerical and computational modelling, experimental innovations, our collaborations with
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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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your application! We are looking for a PhD student in evolutionary genetics interested in contributing to a better understanding of the mechanisms that shape mutation rates. Your work assignments
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transformations of industrial importance. The PhD candidate will become a member of an enthusiastic interdisciplinary research team, which combines experimental and computational tools in the development of novel
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or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale experimental data are now available or it may use population-scale genetic, clinical, or public health
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areas, engaging in both theoretical and experimental research in: Data-driven and learning based control - Data-driven adaptive motion planning - Cognitive reasoning, symbolic knowledge representation
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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable
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molecular mechanisms that drive its invasive behavior, both general and patient-specific. Using cutting-edge spatial techniques and CRISPR-based methods, we build data-driven models that link gene regulation
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We are searching for a doctoral candidate eager to take part in crossdisciplinarity work within battery technology for a sustainable future. This work will compose both theoretical and experimental
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-level data on single cell level, R/Bioconductor and Python/Tensorflow will be useful but is not a requirement. An experimental researcher with an interest in methodologically driven cancer research