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Field
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constantly evolving discovery pipeline. AI-driven interpretation will exploit the full complexity of the dataset to expand the druggable antiviral target space. We seek a researcher with strong machine
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academic partner in AF-B-STEP, contributing expertise in AF burden quantification, AI-based rhythm analysis, and digital biomarkers. The department has a strong data science environment, with an additional
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AF management. Your colleagues: The Department of Physiology at Maastricht University is a key academic partner in AF-B-STEP, contributing expertise in AF burden quantification, AI-based rhythm
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field experimental ecophysiology measurements Experience applying ML/AI to biological or environmental data (e.g., transformer, state space models, multi-layer perceptrons, convolutional neural networks
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researcher will investigate how AI embedded in physical entities, such as robots, vehicles, or sensors, form representations of space, motion, and context through continuous, multimodal interaction with
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. We are committed to free speech and to academic freedom, believing that our foundational purpose as a university, is to create spaces where a wide range of ideas, including ideas that are controversial
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and AI methods for materials discovery. Design and implement active-learning workflows for autonomous exploration of materials spaces. Develop and evaluate generative AI models for inverse materials
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transformation INESC-ID promotes cooperation between academia and industry by addressing research on daily life issues, such as healthcare, space, mobility, human language technologies, agri-food, industry 4.0
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Job description Offshore wind energy is expanding rapidly, with larger turbines, higher power densities, reduced spacing between wind farms, and deployment in deeper waters. These developments
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intertwined physical, social and economic challenges, such as a relatively uniform, ageing housing stock, deteriorating public spaces and infrastructures, decreasing residential attractiveness, eroding social