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uncertainty from climate projections into land-use forecasts. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models
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has a strong background in control engineering, with documented expertise in optimal control, adaptive control and online optimization, stochastic systems, Bayesian inference, and state estimation and
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MOFs and their hybrid materials. The primary objective is to explore the fundamental structure–property relationships that govern MOF vitrification and liquid-phase behavior, and to develop new MOF-based
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services, aiming for maximum resilience. The Postdoc will focus on the following key research objectives: Create a framework for analysing and designing satellite system solutions in cases involving Danish
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with focus on reaching a shared objective, and to achieve a good work-life balance by managing the demands of an ambitious career alongside personal well-being, hobbies, and family time. In terms
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, preferably with a focus on batteries, control, or optimization. Preferred qualifications include: Expertise in multi-domain and multi-objective optimization and AI-based control Strong analytical and modeling
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monitoring scientific progress and ensuring that project objectives and milestones are met. Your competencies The candidate must have a strong background in control theory, reinforcement learning, and game
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-invasive battery lifetime prediction through acoustic emission detection. This involves modeling the battery as a 3D acoustic landscape and tracking its changes over time. The expected outcomes
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tasks You will join the Grand Solution project 'DRONES: Drone-Obtained Electromagnetic Signatures' to exploore innovative approaches such as drone-based radio testing for large objects and EM signiture
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at Aalborg University’s research portal . Job description: The candidate should have a proven track-record for working with both pain and biomechanics. Additionally, the candidate should have