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Developing a non-coding RNA predictive biomarker in bladder cancer School of Medicine and Population Health PhD Research Project Competition Funded Students Worldwide Dr Jonathan Griffin, Dr Dan
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1 Aug 2026 Job Information Organisation/Company UNIMORE Research Field Medical sciences » Medicine Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 26 Aug 2026 - 13:00 (UTC) Country Italy Type of...
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maintenance of command-line and web-based applications deploying machine learning (ML) models for chemical synthesis planning, property prediction, and molecular design and will work closely with faculty
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Predicting reservoir and wellbore scaling in geothermal developments through integrated subsurface interpretation and modelling Award Summary 100% home fees covered, and a minimum tax-free annual
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directions include: ● Generative models of cell–cell communication . Move beyond descriptive ligand–receptor analysis to models that predict and help understand how cells react to signaling cues. ● Joint
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intelligence, as it develops model learning methods based on optimization, together with predictive validation procedures designed to assess the generalization ability of models on new data. Current approaches
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importance when predicting the behaviour of fuel during LOCAs. The project will aim to achieve the following: A thorough literature review on the state of the art of fuel performance codes with a particular
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precipitation, leading to scaling within reservoirs and production wells that reduces operational efficiency. Predicting where these processes will occur is a challenge because they are controlled in part by
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de València announcing a selection process for an indefinite employment contract within the framework of a Line of Research: R&D&i Line: Machine learning for the development of predictive artificial
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: Department of Biology (Professor Laura Pollock ) Position Summary: Develops and implements species distribution models (SDMs) to predict the geographic distributions of tropical plant species using