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traffic flow theory with machine learning, and with that, the best of both worlds: theory and logic where necessary, data-driven where possible. This innovative new approach enables more efficient and
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on type, size, location of data centers in the US, their electricity and water demand, carbon emissions; exposure to air pollution. ● Develop and/or apply methods for causal inference and machine learning
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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techniques; large data processing and high performance computing; advanced causal inference and statistics; computer vision and novel applications of machine learning. Advanced knowledge of R or Python is
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Intelligence and Machine Learning. Join a team of scientists at the leading macromolecular crystallography beamlines and at the computing center and contribute to science projects at the interface between AI
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“big data” allowing agnostic and dynamic collection of information, to deliver a new class of research that will enable a better understanding of the clinical, molecular, behavioural and environmental
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Integration group led by Dr. Jędrzej Szymański. Our group specializes in machine learning, multi-omics data integration, and the development of predictive models for plant gene regulation. We are part of
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an enthusiastic and highly motivated researcher. You have, or will shortly, acquire a PhD degree in the field of Human-Computer Interaction, Interaction Design or Interaction Technology or equivalent Ideally, you
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven