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at the intersection of mathematics, computation, and cancer biology. We develop mechanistic, predictive models of cellular decision-making to address fundamental and translational challenges in cancer, including drug
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. The successful candidate will contribute to the development of predictive numerical models and experimentally validated designs for intricate mechanical assemblies, deployable structures, compliant mechanisms, and
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, and clinical informatics, with healthcare innovation and industry partners. Our research on early prediction of dementia (https://www.bbc.co.uk/news/health-57934589 ) has the potential to deliver
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models to predict errors in auditing. This will cover all stages of the project life cycle, including data acquisition, pre-processing, model development, model testing, and reporting of results
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in New York, across the nation, and around the world. Position Function The CNCPS was modified to be able to predict nitrogen (N) and phosphorous (P) excretion, and this was published by Fox et al
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knowledge of statistical and computational research methods (e.g., predictive/user modeling), cognition/affect tracking, measurement theory, analyzing high-frequency time series data, experience with
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Reactor) designs. This system constitutes a corium containment strategy in the event of a severe accident. Therefore, understanding the thermodynamic properties of the corium is essential for predicting its
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background in nonlinear optics, ultrafast photonics, and integrated photonics, alongside the ability to develop predictive models for optical materials and photonic devices. The successful candidate will work
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. For the Working Group “Ecosystems in Transition” in RD1 at PIK, we are offering a PhD position (m/f/d) (Position number: 10-2026 PhD ARTECO) in the field of ecological modelling of complex arctic landscapes
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speech processing, a research question that is rapidly gaining in importance. The project is centred on the hypothesis that the cerebellum conveys predictions about upcoming speech sounds to the neocortex