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Sharing – Building a federated data space to enable responsible data integration and cross-project learning. AI & Modelling – Using shared data to power advanced models that help describe and predict
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at Cornell University is seeking a Postdoctoral Associate to advance research on maize and grass molecular diversity using genomic large language models (AI). The goal is to design nitrogen-efficient maize
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National University of Science and Technology POLITEHNICA Bucharest, Pitesti Branch | Romania | about 1 month ago
on modeling, simulation and advanced analysis of energy conversion systems. The activities include studying the effects of low-quality power supplies and ripple current on electrolysis systems, developing
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Description Context Federated learning (FL) enables models to learn from distributed datasets across diverse clients (e.g., edge devices, hospitals, or industrial sites) while maintaining privacy [1]. A major
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and validation of a predictive pipeline for excipient–biologic interactions Integration of experimental SAXS data with AI-driven structural modeling to predict oligomerization behavior and excipient
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computational modelling and non-invasive brain stimulation. The focus of this project will be on advanced versions of transcranial alternating current stimulation (tACS), targeting multiple brain areas in
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measured depends on clinical decisions that vary between hospitals, physicians, and patient states. As a result, previous models have struggled to generalise beyond the hospital they were trained on. We
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Description The Buckler/Romay Lab at Cornell University is seeking a Postdoctoral Associate to advance research on maize and grass molecular diversity using genomic large language models (AI). The goal is to
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business problem scoping to deployment and monitoring of production-grade models-with a focus on both Generative AI and Deep Learning. The ideal candidate holds a Ph.D. in Deep Learning or Generative AI and
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investigate HIV-induced vascular dysfunction and neuroAIDS molecular mechanisms using in vitro and in vivo (animal models) approaches. Specifically, the person will utilize HIV mouse models and HIV-1 infected