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Field
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: A good Bachelor’s degree or Master’s degree in Computer Science, Engineering, or a related discipline, with demonstrated experience in Artificial Intelligence (AI) / Machine Learning (ML) research
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of solid-state materials, with experience in density functional theory (DFT) and/or machine learning interatomic potentials. We welcome applicants with a broad range of research interests and experiences who
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. Develop AI, machine learning, optimisation, and decision-support algorithms. Build digital twin environments, physics-based modelling, concept evaluation frameworks, and technical risk assessments. Develop
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of metabolism, membrane transport, cellular regulation, structural systems biology, machine learning and disease mechanisms. These positions are embedded in the transition from CeMM in Vienna to the newly founded
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. Specific topics of focus include, but are not limited to, linear response, statistical limit laws, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics
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modalities in the rodent brain (3D light-sheet microscopy, spatial transcriptomics and MRI of the same brain) to learn MRI contrasts that can depict neuronal and glial cell density and morphology. You will
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An opportunity has arisen for a Research Fellow in Machine Cognition in UCL Linguistics, within the Division of Psychology and Language Sciences and Faculty of Brain Sciences. UCL is ranked eighth
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, machine learning, or related fields. Have strong programming and computational skills in Python, R, MATLAB, or related analytical and machine learning frameworks, such as PyTorch, TensorFlow, or Scikit
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly
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an ambitious researcher with expertise in artificial intelligence, machine learning, or computational statistics, and a demonstrated interest in applications to human genetics and/or population health. We