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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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for the traineeship This fellowship aims to advance the development of a risk-informed decision culture at ESA and application of probabilistic frameworks in the planetary protection domain. The overall goal is to
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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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attention mechanisms Strong background in probabilistic machine learning Proven track record in time-series analysis and modeling, signal processing, medical domain, and/or related fields Strong writing
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synthesis of timed and probabilistic behavioral models for model checking, performance evaluation, and optimization. The overall objective is to establish formal foundations that bridge static engineering
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Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field observations—including
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qualifications: Experience with data assimilation, probabilistic machine learning, Bayesian inference, inverse modeling, and/or simulation-based inference is an advantage. Experience with land-surface models
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is required Desired qualifications: Experience with data assimilation, probabilistic machine learning, Bayesian inference, inverse modeling, and/or simulation-based inference is an advantage
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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