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Field
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for roughly 3% of global CO₂ emissions, and with international pressure to decarbonize hard-to-abate sectors, nuclear propulsion is emerging as a serious candidate for deep-sea and high-utilization vessels
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setup, validate results using electrophysiological recordings, apply deep-learning approaches to analyse noisy data, and disseminate your findings through publications. The team – You will work with the
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, methods and models (e.g., deep learning methods, multimodal AI) for the automatic identification of behavioral cues during ecological interactions with people and the environment and analyses of video and
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UiO/Anders Lien 3rd July 2026 Languages English English English PhD Research Fellow in Active Learning for Arctic observing systems Apply for this job See advertisement About the position Position
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under internal company contract and in external collaborative projects etc. Key Qualifications: PhD in computer science specialised on data sharing, digital twins, distributed databases, AI, or a related
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behaviour, social change or related fields. You will be an experienced researcher with a PhD or equivalent research expertise gained through academic, independent or professional practice. With a strong track
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for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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supporting documentation, proven experience in all of the following areas: Computer vision and video processing (ingestion, ROI, 2D/3D keypoints, heatmaps); Deep learning and temporal modelling (CNNs
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algorithms, representation and learning of data and dynamical systems, geometric deep learning, topological and algebraic data analysis, optimization on manifolds, and operator- and PDE-based approaches