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provide meaningful explanations of their predictions to downstream users. A central aim of this project is to investigate how Vision-Language Models (VLMs), Multimodal Large Language Models (MLLMs), and
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antigens presented by MHC molecules. This curiosity-driven project develops cutting-edge AI technology for 3D TCR–pMHC modeling to improve neoantigen identification, TCR specificity prediction, and TCR
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planetary atmospheres. The Section also initiates and manages a wide range of related modelling, software and hardware R&D activities. You are encouraged to visit the ESA website: https://www.esa.int/ Field(s
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. The second project is “Forecasting Fertility with Foundational Models”. More specific tasks include: Conducting independent high-quality research on the limits of predicting fertility in relation to the PreFer
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Position Data Science and Artificial Intelligence in Cardiac Electrophysiology Our goal: the development of multimodal prediction models to detect and predict atrial fibrillation (AF) and other clinically
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of multimodal prediction models to detect and predict atrial fibrillation (AF) and other clinically relevant cardiac rhythm patterns, predict disease progression and treatment response, and support personalised
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Challenge: Predict long-term cardiac growth and remodeling in cardiovascular disease. Change: Develop multi-scale mechanobiological models for virtual human twins. Impact: Advance patient-specific
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cloud cover may amplify rather than mitigate global warming but the magnitude of this is highly uncertain. One of the greatest challenges in climate science is to predict how clouds in general, and
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a postdoctoral researcher, you will: Integrate hybrid traffic models and data assimilation methods into a coherent prediction framework. Develop uncertainty quantification methods and explainable and
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path-flows, and other key parameters and inputs. In your role as a postdoctoral researcher, you will: Integrate hybrid traffic models and data assimilation methods into a coherent prediction framework