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Area of research: Scientific / postdoctoral posts Job description: GFZ is Germany's national centre for solid Earth research. We advance the understanding of dynamic processes to address global
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developers, electrical and mechanical engineers. Experience and strong understanding of machine learning algorithms, mathematical modelling, and applications of AI. Proficiency in Python, leading ML frameworks
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within a cross-functional team, including software developers, electrical and mechanical engineers. Experience and strong understanding of machine learning algorithms, mathematical modelling, and
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. This will require both technical insight into data modeling and a solid understanding of how real-world engineering data is generated, structured, and used. For the postdoc position, we are looking for a
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the gene regulatory networks underlying early human postimplantation development, with a particular focus on the mechanisms of X-chromosome inactivation and cell fate specification in extraembryonic lineages
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policies pertaining to other schools at Duke University. The postdoc candidate is expected to: 1) Develop novel methods for incorporating scientific machine learning in solving problems in solid mechanics
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on Experimental Study and Computational Solids Mechanics model of soft multifunctional materials such as magneto-active polymers (MAPs), Electro-active polymers (EAPs). The successful candidate will work on the
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the polymers will be investigated to discover earth-abundant and synergic metal combinations and to elucidate the minimum energy inputs to enable effective polymer production. The research will involve catalyst
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simulations to (i) minimize the discrepancies between measured and modelled historical ocean carbon sinks, (ii) elucidate key mechanisms driving BCP changes, and (iii) constrain the uncertainties in Earth
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Area of research: Scientific / postdoctoral posts Job description: GFZ is Germany's national centre for solid Earth research. We advance the understanding of dynamic processes to address global