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Field
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Jozwik lab studies visuo-semantic cognition combining cognitive science, neuroscience, and computational modelling. The lab's research has focused on probing specific visual dimensions in the context
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of data scientists, software engineers, and experimental researchers on topics including: Developing multi-scale and multi-modal representation learning methods for scientific imaging data (e.g., SEM
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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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Description Probing neural representations of speech, in parallel with experimental phonetic analysis of the same data (collected in the field); placing the results in the context of linguistic typology. In
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trigger redesigns across multiple groups. The challenge is compounded by the fact that each discipline uses different data models and representations, making system-level interdependencies difficult
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tools without disrupting established practices? iii) Technology Assessment: To what extent do representational approaches such as ontologies or knowledge graphs meet the identified requirements, and where
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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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Experience with deep learning for image analysis and/or medical image processing Knowledge of self-supervised learning, representation learning, and/or generative models Experience with multimodal machine