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wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
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density) influence energy dissipation develop mathematical models to predict and explain these effects collect and analyse data, including with the use of machine learning use this knowledge to design
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at RIBES using machine learning and genomics to overcome linkage drag and accelerate crop breeding by design. You will build models, analyse new sequencing data and optimise strategies for combining
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Are you interested in cutting-edge research at the intersection of large language models, deep learning, and information retrieval? The Information Retrieval Lab (IRLab) at the University
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modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an innovative startup partner. You will be
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intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 277494287 Position: Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in
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) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation
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”. Are you excited about the opportunities that data science in nutrition—especially the role of targeted metabolic modelling, machine learning and AI offer in developing effective personalized nutrition
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. Responsibilities: Conduct research in compilation, optimization, and analysis for time-predictable computer architecture. Co-supervise MSc and PhD students. Contribute to teaching and research proposal preparation