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, evaluate, and deploy supervised classification models (e.g., XGBoost, LightGBM), design ensemble pipelines combining rule-based, probabilistic, and ML components; apply explainability tools (e.g., SHAP, LIME
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opportunities within the company. Responsibilities Develop and implement advanced computational and machine learning strategies, including deep learning, graph-based methods, and probabilistic modeling
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is part of the ERC-funded project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD
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Project 7: Probabilistic Representation Learning Employment: UiT - The Arctic University of Norway, Department of Physics and Technology PhD programme: UiT - The Arctic University of Norway, Faculty of
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Starting date: preferably in 2026, by agreement Project 7: Probabilistic Representation Learning Employment: UiT - The Arctic University of Norway, Department of Physics and Technology PhD programme: UiT
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, matched to energy supply and user demands. The project will include optimization and probabilistic analysis under practical engineering constraints. You will develop new tools for network optimization and
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: Probabilistic generative models (VLMs, diffusion, flow models) Reinforcement learning & Markov decision processes Causal inference & counterfactual reasoning Mechanistic & physics-informed modeling Agentic AI
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long-term experience in investigating the terrestrial cryosphere and is involved in various high-level research projects and centers. (See for instance http://www.mn.uio.no/geo/english/about/organisation
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into embryonic structures - Molecular biology techniques - Advanced imaging - Single-cell sequencing - Drug treatments and effect analysis Where to apply Website https://pica.cineca.it/ Requirements Additional
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of experimental data generated through immunocytochemistry, molecular biology, and imaging techniques. Where to apply Website https://pica.cineca.it/ Requirements Additional Information Eligibility criteria