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Postdoc: Machine learning for wind flow prediction in coastal dunes Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline: 6
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communicate predictions about human behaviour, contributing to trustworthy intelligent systems for high-impact applications. Job description Human behavior understanding is becoming an essential component
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predictive MD simulations capable of resolving atomic-scale LCI mechanisms with near-DFT accuracy Investigate how silicon suppresses LCI, including its effects on grain boundary site competition and the
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Functional Theory (DFT)-accurate machine-learned interatomic potential (MLIP), enabling predictive MD simulations capable of resolving atomic-scale LCI mechanisms with near-DFT accuracy Investigate how silicon
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demographic research has developed numerous theories of fertility and produced a sophisticated body of work on the many characteristics associated with fertility. Despite this, we are poorly able to predict
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body of work on the many characteristics associated with fertility. Despite this, we are poorly able to predict individual-level fertility and forecast future levels of fertility. The postdoctoral
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complex systems, will you help us develop a new generation of road traffic prediction methods? Job description Road traffic is a highly complex dynamic system. Minor disruptions can lead to major delays
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Hey machine learning enthusiast with a love for physics and complex systems, will you help us develop a new generation of road traffic prediction methods? Job description Road traffic is a highly
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data to learn contextual representations of microbes and communities and translate them into predictive models for successful crop microbiome engineering. Your job Plant-associated microbiomes can
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microbiome and genome data to learn contextual representations of microbes and communities and translate them into predictive models for successful crop microbiome engineering. Your job Plant-associated