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Field
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energy systems and demand response; process and energy system modeling; optimization and model predictive control; and applications of artificial intelligence and machine learning to energy systems
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/2026 Group or Departmental Website: https://med.stanford.edu/supekar-lab.html(link is external) How to Submit Application Materials: Please email application materials as a single PDF to supekarlab
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contribute to our ongoing projects, specifically on development of nanoparticles with predictable interactions with biomolecules and understanding nanoparticles behaviour in biologically relevant environments
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collaborate, each with their expertise, to carry out a scientific activity with a shared research goal. The Nanochemistry Research Unit (https://www.iit.it/research/lines/nanochemistry ) at IIT in Genoa
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data. Leading statistical analysis (methods may include IPTW-weighted Cox regression, active comparator new-user designs, self-controlled case series, propensity scoring). Developing and extending an AI
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, the mechanical state of the wall, or a defined combination of both. The resolved model will also predict the conditions under which the wall fails, with direct relevance to controlled, low-energy cell disruption
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learning, understand their mathematical foundations, and connect them to space-related technologies and missions. The focus is on building rigorous models that explain and predict the behaviour of modern
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of computational tools for process monitoring and predictive analysis Candidate Criteria Applicants should have: A PhD in Process Engineering, Chemical Engineering, or a closely related field Strong expertise in
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Post Doctoral Researcher Rinn Artificial Intelligence – Research & Innovation in Data Science and AI
patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics) • Multi-omics for non-cancer health screening applications, • Machine learning modelling
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
the Sustainable Materials Research Center (SusMat-RC) at UM6P. The successful candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive