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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
independently, has a passion for AI and its applications, and is willing to learn new technologies. The candidate should have a PhD in Computer Science or a closely related field. Relevant background and skills
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preliminary analysis of the data using graphs, charts or tables to highlight the key points of the research results collected in accordance with the research protocols as stipulated. Prepare and present
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holding a PhD in computer science or artificial intelligence, with a strong taste for formal modelling. Experience in several of the following areas will be particularly appreciated: symbolic AI (planning
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must be met no later than the time the employment decision is made. At least three of: (stochastic) partial differential equations ((S)PDEs) graph theory/network science stochastic optimization and
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programme. Requirements: Education: PhD in chemistry, chemical engineering, materials science, biochemistry or equivalent. Knowledge: Extensive expert use of an array of physicochemical and materials
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manuscripts, grants and train undergraduates in the lab is desired. PhD in a biomedical science such as physiology, exercise science, pharmacology, or immunology earned by the start date. Research experience in
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requirements, what we are really looking for is a creative, quantitatively strong scientist who likes to invent. Your PhD might be in physics, computer science, computational or systems biology, bioinformatics
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. Experience applying machine learning to networking problems, for example, reinforcement learning, graph neural networks, or uncertainty quantification. A track record of publications in leading networking
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Foundation Health Model. As a PhD candidate, you will conduct deep-dive research into training pipelines and reasoning techniques for clinical foundation models. You will join an elite, interdisciplinary team
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. Experience applying machine learning to networking problems, for example, reinforcement learning, graph neural networks, or uncertainty quantification. A track record of publications in leading networking