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MSCA Doctoral Network position “Electric solar wind sail mission design” We are now looking for two (2) Doctoral Researchers. This PhD topic is part of the “Electric solar wind sail doctors” (E
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Development of reproducible software tools and computational workflows for biomedical research Develop novel AI, machine learning, and deep learning methods to address complex biomedical questions in diabetes
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applied research in the domains of food and biobased materials. Where to apply Website https://www.academictransfer.com/en/jobs/364061/machine-learningai-researcher-a… Requirements Specific Requirements You
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agricultural economics who can translate complex policy challenges into rigorous research programmes, lead multidisciplinary teams, and communicate findings clearly to decision-makers. You bring deep knowledge
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regulation, with the long-term goal of enabling more precise design of RNA therapeutics for complex diseases. The doctoral researcher will analyse large-scale single-cell and bulk multi-omics datasets
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of heterogeneous technologies are increasing the complexity of power delivery, thermal management, and energy optimization across the entire computing stack. Emerging technologies such as fine-grain 3D integration
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administrative workload, the standardization of complex assessments, and reduced professional discretion. There is therefore a need for more research examining how digitalization unfolds in practice. The PhD
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Network that aims to bring Electric solar wind sail (E-sail) from Low Earth Orbit (LEO) demonstration missions to the operational environment of the solar wind. It is delivered by 8 universities in
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educational technology spaces can foster embodied and epistemic engagement with scientific practices – helping students not only understand complex concepts and procedures in the short term, but also develop
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
-informed/physics-guided models to complex engineering systems Development and use of advanced scientific AI methodologies, including surrogate models, generative models, probabilistic models, and both data