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) training personalized computational models in new contexts, and (iii) studying in-silico clinical intervention strategies. The postdoctoral fellow will have the opportunity to: Learn about computational
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and techniques in archaeology. The project has aimed to develop a two-part research programme. The first part focuses on developing and applying methods for 3D digital modelling, ranging from
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training covering topics such as computational modelling, numerical methods, statistical analysis, machine learning or data-driven analysis of complex systems Experience 0–3 years of postdoctoral experience
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machine learning within the Department of Electrical and Computer Engineering. The Postdoctoral Fellow conducts independent and collaborative research, publishes findings in peer reviewed conferences and
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
close collaboration with a specific group (DARSA) specialized in developing and applying remote-sensing tools and innovative open-source machine-learning methods. Key responsibilities Develop effective
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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metaproteomics approaches Analyzing large-scale multi-omics and clinical datasets to investigate individual metabolic responses to diet. The work includes applying advanced statistical and machine learning methods
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industrial decarbonisation modelling to support the EU-funded FLARE project. The role will lead the technical development and integration of bottom-up, organisation-level decarbonisation models for energy
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, including exoplanet studies, machine learning, cutting-edge radio instrumentation and digital signal processing, citizen science, sky surveys, and studies of transient and variable objects. Listen is deeply