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or further information feel free to contact us. Job description: - Application of specially developed approaches to define transferable force-fields with machine learning for different classes of complex
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for the physical sciences. We have a strong profile in computational statistics, simulation and learning algorithms, and scientific software development. As a closely collaborating, international team
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. Numerical models of varying complexity and different observational data sets are used to study atmospheric processes and phenomena on time scales ranging from single weather events to long term climate change
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differences between ascarid species during the life cycle and whether these differences are intrinsically imposed or depend on metabolic cues in the host microenvironment. To assess metabolic pathways in tissue
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of the novel methods created within our research group. Your primary tasks will include: - Assisting in the research taking place in our group. - Collaborating with our researchers to translate new algorithms
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macromolecular dynamics with statistical mechanics, molecular simulation at different resolutions, machine learning, and experimental data. Our group works on the definition and implementation of strategies
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animals. Our strategy will allow us to compare stress responses and behavioural data between different species and classes of pathogens, leading to important synergies by using standardised methods
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to control the highly endemic Ascaris spp. infections. Based on recent own findings ascarids, when compared to other intestinal helminths such as strongyles, obviously exhibit a different mechanism of
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. Requirements: • Bachelor's degree in computer science or equivalent knowledge Desirable: • Knowledge and practical experience in areas such as algorithms, data structures, software engineering, artificial
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The research unit Macrosociology investigates differences in attitudes and behaviour between European countries and over time. Our main research endeavour is to understand (1) why citizens from