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broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data
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French National Research Institute for Agriculture, Food, and the Environment (INRAE) | Villenave d Ornon, Aquitaine | France | about 2 months ago
be to develop predictive models dedicated to the ecological transition of agriculture. The project extends the understanding of links between metabolism programming and trade-offs with plant
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speech processing, a research question that is rapidly gaining in importance. The project is centred on the hypothesis that the cerebellum conveys predictions about upcoming speech sounds to the neocortex
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model predictions with biological knowledge and external data sources. Work closely with academic partner groups and the Innovation & Business (I&B) team to align technical development with biological
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PhD researcher in AI:GENOMIX, you will contribute to next-generation models that rethink polygenic prediction and support the future of precision medicine. AI:X is an ambitious initiative at Aalborg
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University (ASU) invites applications for a postdoctoral scholar position associated with the US NSF Center for Analysis and Prediction of Pandemic Expansion (APPEX) in areas associated with mathematical and
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. The project combines interval timing and error monitoring in a novel behavioral task adapted to human and rat models. The leading hypothesis is a read-out model, which assumes that a Timer and a Reader are
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computational methods to address and derive theoretical models and predictions. For this line of research we are seeking several postdoctoral researchers to work synergistically both within the team and with our
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robust descriptors (e.g., water activity, sorption, glass transition temperature, plasticization, porosity, internal distribution) and provide predictive guidelines to rationally select and design drying
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: Textual Prediction of Survival (LLM classification & Attention Modelling) This project develops a model to predict patient survival by analyzing heterogeneous clinical documents. Unlike traditional methods