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, analytical chemistry, neuroethology and collective animal intelligence to develop predictive models on honeybee behaviour in response to chemical cues. If you care about biological diversity, sustainable
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-edge generative and predictive methods for materials discovery, we offer an excellent opportunity to advance your research. We are seeking a highly motivated and talented postdoctoral researcher to join
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predictive framework linking genomic data to extinction risk, working at the interface of evolutionary genomics, simulation modelling, and machine learning. By integrating forward-in-time simulations, real
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potential for exploiting temperature gradients for producing electricity and predict their long-term performance under real operating conditions. The project also includes modeling of heat transfer and
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to prevent disease and promote health by developing models and methods within the area of risk benefit assessments of foods. This includes epidemiological modeling with the aim to predict and prevent infectious
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execution time (WCET). The postdoc position focuses on compiler support for WCET analysis for time-predictable architectures such as Patmos/T-CREST. Furthermore, it is expected to join the development of a
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Sea-Level Predictions (CISP) lead by DTU and include the Geological Survey of Denmark and Greenland, the University of Copenhagen, and Dartmouth College in the United States. CISP combines data
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-of-the-art practical engineering models for predicting sand and particle transport struggle with the cross-shore processes (perpendicular to the beach), and they even have difficulties predicting the sign
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protein design tools to generate and prioritize inhibitor candidates with high predicted binding and selectivity. These designs will then be experimentally validated through a combination of affinity
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systems Strong skills in data-driven analysis and modelling, simulation, control, and validation Familiar with modeling of PtX and storage technologies, model predictive control, machine learning