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Research Framework Programme? Horizon Europe - ERC Is the Job related to staff position within a Research Infrastructure? No Offer Description The Machine Learning for Integrative Genomics team (https
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mechanical loading of such samples. The focus of the PhD project will be to use machine learning techniques to better understand the interplay between the crystal orientations and deformation patterns in a
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outcomes Conduct applied research in areas like information extraction, machine learning, and artificial intelligence, exploring their applications in the context of social media and cross-platform
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integration analytics, machine learning, and/or AI. In addition to carrying out research, the successful candidate will be expected to apply for fellowship funding, contribute to the writing of grants and
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the following areas desirable but not essential: electrocatalysis, rheology, coating technology, machine learning Intrinsic motivation to show initiative, creativity, and to work independently Excellent
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We are looking for a PhD student in Visual Learning and Communication with a focus on interactive visualization, visual learning, science communication, and educational science, formally based
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FAIR principles, combined with skills in statistical analysis, machine learning and/or data science. Experience with programming languages such as R, Python, or similar will be considered an advantage
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Degree Doctor of Philosophy (PhD) / Doctor rerum naturalium (Dr rer nat) Course location Hannover In cooperation with Twincore - Centre for Experimental and Clinical Infection Research, University
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and machine learning techniques for the design of superconducting qubits, one of the leading qubit modalities used in today’s quantum computers. The optimal design of superconducting qubits is a highly
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | 24 days ago
-scale (~10’s of km2) permafrost thermo-hydrological hybrid twin, to be coupled with state-of-the-art freezing/thawing soil mechanics machine learning-based surrogate models (Richa et al., 2024, Tristani