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Field
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The ultimate objective of this project is to detect cognitive strategies in single-trial EEG data. In very broad terms, we will use the HMP method (https://github.com/GWeindel/hmp ) to discover
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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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of the future will increasingly rely on integrated digital systems that combine practical expertise with real-time feedback and predictive insights. At BFFI, we aim to improve CEA system design and crop
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data to learn contextual representations of microbes and communities and translate them into predictive models for successful crop microbiome engineering. Your job Plant-associated microbiomes can
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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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is to detect cognitive strategies in single-trial EEG data. In very broad terms, we will use the HMP method (https://github.com/GWeindel/hmp ) to discover cognitive operations in existing data, label
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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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with researchers across the consortium to predict and edit targeted gene promoters, to design relevant constructs for gene editing and to evaluate the gene-edited plants to establish this topic firmly into the plant
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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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activities, all generally but not exclusively related to your research topic. You are encouraged to visit the ESA website at https://www.esa.int/ Field(s) of activity/research for the traineeship The objective