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and materials engineering. They will do this by integrating modern data-centric approaches, such as physics-informed machine learning, structure-aware modelling, and digital-twin methodologies, with
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). Responsibilities: • (60%) Lead research projects in bioinformatics and data-driven discovery. o Design and execute bioinformatics workflows for multi-omic datasets, including RNA-seq, scRNA-seq, ATAC-seq, and
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/Computer Engineering, Computer Science, Applied Maths or related. Strong skills in AI techniques/ML/optimisation (Python/Matlab); familiarity with probabilistic modelling, time-series or control/power
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University of California, San Francisco | San Francisco, California | United States | about 15 hours ago
focus on an AI-driven project aimed at improving patient selection for a Transitional Pain Service (TPS). The position bridges cutting-edge artificial intelligence technology with clinical workflows
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Systems Engineering (Health Systems Data Analysis, Modeling, Computing, and Cloud-Based Health Systems Analytics and Decision Support Platforms) as part of the CTECH Postdoctoral Fellows program. This
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simulation tools and draw directly on your research and professional experience. You will guide students from simple baseline and compliance-driven models towards genuinely low-carbon, performance-focused
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. In addition, you must have: a solid foundation in energy technology and a strong understanding of artificial intelligence (AI), machine learning (ML), and data-driven modeling documented experience
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being development of Data-driven production process enumeration framework for SCP production, to benchmark economic, environmental and socio-economic performance. As a part of the project you will
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/ and about the Soft Materials Group at https://www.aalto.fi/en/department-of-chemistry-and-materials-science/soft-materials-modelling More about Aalto University: Aalto.fi youtube.com/user
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prohibitive computational cost. Conversely, simpler models such as the Log- Distance Model provide greater computational efficiency at the expense of accuracy [6]. - Data-driven approaches, which leverage