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at unprecedented resolution. The core innovation of your work will be integrating this data to train deep learning models that predict chromatin accessibility and gene expression patterns. These models will
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integrating modeling, machine learning (ML), and advanced control methodologies. The research will focus on designing AI-driven algorithms to assess battery health, predict degradation trends, and optimize
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, aimed at revolutionizing the way osteoarthritis is understood, diagnosed and treated by developing multimodal patient-relevant endpoints, advanced predictive models, and next-generation clinical trial
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University Duties This PhD project is part of the AFLOW consortium supported by the Swedish Energy Agency and focuses on multi-scale modelling of aqueous organic redox flow batteries, to build a predictive
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CORE A*/A conference paper. We invite applications for a postdoctoral position focused on the development of predictive models for clinical outcomes following Deep Brain Stimulation (DBS) in Parkinson’s
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, Mullins effect, and damage mechanisms. The main objective is to identify biomechanical biomarkers of tissue fragility and to develop predictive models of stiffening and failure, integrating histological
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financial goals through enrollment data analysis, predictive modeling, and decision support. This role combines deep analytical expertise with business acumen to transform complex data into actionable
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to identify novel biomarkers and therapeutic targets, mathematical and computational modeling to predict disease progression and therapy response, the design and validation of innovative therapies, and their
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The project: Non-destructive testing (NDT) is vital to ensure structures are safe and to reduce operating costs by facilitating predictive maintenance. The focus of this project is on improving
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part of the international Refuge-Arctic project (https://www.refuge-arctic.ulaval.ca ) with links to the NASA FORTE project, whose overall objective is to better understand and predict the role played by