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and implementing NLP pipelines for clinical text processing, semantic annotation, and representation learning Developing embedding-based representations of clinical variables and documents to support
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visual representation Excellent written and verbal communication skills Desirable skills and experience: Knowledge of tenement architecture in at least one case study city, or strong familiarity with
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such as Machine Learning, Natural Language Processing, AI in Education, Knowledge Representation, and Python-based analytical seminars at the BSc, MSc, and PhD levels. Responsibilities include assisting in
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, and representation learning Developing embedding-based representations of clinical variables and documents to support semantic harmonisation and retrieval Designing and implementing Retrieval-Augmented
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emulator of sea ice dynamics, trained using high-fidelity numerical simulations, (ii) variational data assimilation methods, and (iii) a simplified representation of physical processes in the atmospheric
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Europe project Earth Observation & Weather Data Federation with AI Embeddings (Embed2Scale) with partners across Europe, will tackle our understanding and compressed representations of clouds in
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on information and digital technologies (computer science, automation, robotics, artificial intelligence). Its aim is to develop methods for the representation, analysis, and control of systems subject to various
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, production and emissions, ensuring data integrity and adherence to agreed procedures Select and apply appropriate data cleaning, representation and validation methods Conduct statistical analyses and model
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reproducible research, collaboration, and knowledge sharing. You should meet the following qualifications: A PhD in Electrical Engineering, Robotics, Computer Science, Artificial Intelligence, or a closely
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research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better understand, explain and advance society and environment we live in. Your