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, with a particular focus on identifying and characterizing rare endosomal escape events. The tasks include developing, training, and validating deep learning–based models for event detection and vesicle
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forutsetter at nettsiden kan huske de valgene du gjør. Du kan velge bort cookies fra Google Analytics her . Det er mulig å velge bort enkelte annonsenettverk som deler opplysninger på tvers av forskjellige
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environment. Key research Objectives: AI Innovation (Taxonomic Identification): Developing and optimizing deep-learning architectures (e.g., YOLO) for the automated detection and classification of nocturnal
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a structured, quality-conscious manner The flexibility to work independently, with self-initiative, as well as collaboratively Ability and eagerness to learn new methods and a strong interest in
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people and the world in which they grow and learn. Current AI tools—often considered “black boxes”—are characterised by a critical idiographic–nomothetic discrepancy. They are trained on static, aggregated
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to work independently, with self-initiative, as well as collaboratively Ability and eagerness to learn new methods and a strong interest in developing both computational and analytical skills Desirable
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bespoke methods – including advanced data modelling approaches (e.g., machine learning, digital twin models) and AI techniques where appropriate – to provide novel solutions that enable sports to make
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description Integreat - the Norwegian Centre for Knowledge-driven Machine Learning at the University of Oslo invites applications for a doctoral research fellowship. The PhD candidate will work at the
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design to improve on-site waste management The development and implementation of initiatives that enhance waste sorting quality and documentation practices The application of analytical, computational, and
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) to ensure efficient roll-out of the system Profile MSc degree in Geophysics, Seismology, Earth Sciences, Physics, Engineering, or a related field Strong quantitative and analytical skills as