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causal analytical foundations of mass transit system performance. The research is expected to contribute both novel methodological advances in statistical modelling, causal inference, and machine learning
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at the atomic scale, using density functional theory-accurate machine-learned potentials and molecular dynamics simulations, in close collaboration with leading European research institutes and steel industry
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reinforcement learning approaches for autonomous guidewire shaping and control. • Design experiments, validation strategies, and quantitative performance metrics for robotic navigation and wire-shaping systems
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. Mandatory requirements: Demonstrated knowledge of machine learning techniques and programming, with extensive experience in data analysis using Python and R. PhD in Economics, with expertise in health
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. This large multimodal dataset allows us to estimate and test different computational models of the decision and learning processes. One postdoc is currently working on the MEG and iEEG data, and one PhD
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Description The Clinical Artificial Intelligence Lab at NYU Abu Dhabi seeks to improve patient care by developing new machine learning methodologies that tackle unique computational problems in
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requirements We will consider applications based on the following skillsets. If you do not fullfil all requirements, please still apply (there is room for learning-on-the-job). A PhD in aerospace/mechanical
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-career scientist to develop cutting-edge machine learning approaches for understanding and designing pathogen antigens. This is a unique opportunity to help shape a new research program at the intersection
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publications prior to the panel interview. In addition to further excelling your skills in Computer Vision/Big Data/Machine Learning analyses, this opportunity enables you to: - Work closely with clinicians
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strands. • Bespoke modelling of tumour metabolic function using 3D and 4D imaging data • Cancer patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics