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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
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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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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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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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for, and mitigating weather and hydrological extremes, as well as to develop effective decarbonization strategies. The possible role of AI ranges from predictive models of solar insolation and wind variables
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on the project can be found here: https://hecustom.eu/ This post will contribute to the creation and validation of a digital twin (with biological bone models) to assess and interrogate the issue of
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, biophysical modelling, clinical prediction and digital health. The role spans three connected dimensions: research leadership, clinical integration and education. The post holder is also expected to work
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . The research associate will focus on Vision-Language Model based situation awareness and decision-making
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at the surface of various materials. Key Responsibilities: Perform quantum mechanical calculations (DFT) for establishing reaction mechanisms and kinetics Develop and apply advanced computational models to predict