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Field
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, rental platforms, and production systems—where decision-making must balance conflicting objectives, leverage real-time data, and ultimately support sustainable profitability. Examples include optimizing
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methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon
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In this role, you will be responsible of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with
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between renewable energy and biodiversity objectives. Your tasks and responsibilities are: identifying and analysing essential biodiversity indicators relevant for spatial energy planning across different
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to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute
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observing systems to adapt and learn from data - identifying which measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling
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develop methods to spatially identify areas of synergy and conflict between renewable energy and biodiversity objectives. Your tasks and responsibilities are: identifying and analysing essential
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Posting Details Position Details Title Postdoctoral Researcher, Astronomy Appointment Status Non-Tenure Track Department IU Bloomington Astronomy Location Bloomington Position Summary The Department
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methods to ultimately let dermatologists continually update multi-modal machine learning models. Our research objectives are to 1) develop novel model editing methods for multi-modal models, with a focus on
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with Bayesian/ML methods is a plus Proactive, collaborative communicator with sound statistics, problem‑solving skills, and a commitment to research integrity and open, reproducible science We offer