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English. A basic training in one of the following fields; polymer synthesis, polymer characterisation, machine learning or high throughput experimental platforms will be of advantage. Admission Regulations
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, enzymology, or molecular biology - Experience with computational methods (e.g. de novo protein design, molecular modelling, machine learning, or bioinformatics) - Experience with biochemical or biophysical
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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and
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dimensions andanalyse particle trajectories using a combination of established tracking algorithms and machine-learning-based approaches. You will further correlate the diffusive behaviour of viruses
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the Arctic, experimental tests of climate driven changes in carbon export from land and turnover and release of greenhouse gases (CO2 and CH4 ) from headwaters, and use of machine learning and process-based
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to machine learning is well funded and continuously publishes in high impact journals. We foster a creative working environment, where you will find freedom to implement, develop, and publish research
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and CH4) from headwaters, and use of machine learning and process-based model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed
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application! We invite applications for a fully funded PhD student position to join the research group of Andrew Winters to work on challenging problems in Computational Mathematics for accurate and reliable
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communications theory methods Applying optimization techniques and machine learning/AI approaches Conducting simulations and experimental validations Collaborating with Ericsson and Chalmers researchers Publishing
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, network science, and decentralized machine learning. Welcome to read more about us at: https://liu.se/en/organisation/liu/isy/ks . For more information about working at ISY, please visit: https://liu.se/en