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thesis is not pre-defined and should be defined by the candidate over the course of the first year. The domain of the PhD thesis must be machine learning and either control theory, path planning, or multi
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Water Fluxes. Your tasks Build hybrid models, process-based and deep learning models, to capture ecosystem flux dynamics across space and time Develop generalizable models robust to climate variability
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Engineering, Machine Learning, Artificial Intelligence, Computational Linguistics, or a related field) • Strong programming skills (e.g., Python) • Strong skills in machine learning, deep learning and modern
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-acoustic waves are weak, noisy and broadband. To address these challenges, this project will employ deep-learning techniques for signal denoising and 3D dose reconstruction. The project is in close
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) Radio/physical-layer intelligence (e.g., channel estimation, CSI prediction, edge-deployable deep learning), or ii) Networking and control-plane intelligence (e.g., reinforcement learning for scheduling
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twins, human-centric systems, robotics PhD-E: Optimizing Images Quality and Deep Learning Methods for Vineyard Disease Detection. PhD grantors: University Padova (IT) & Poznan University of Technology (PL
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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meaningful feedback to patients, clinicians and policymakers The PhD will work at the interface of machine learning, deep learning, geospatial AI, causal modelling, and digital health systems. Your Role You
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Description We are seeking a motivated new PhD candidate who wants to join an exciting collaborative research program within the VIB-Center for Inflammation Research between the Guilliams, Saelens
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Referensnummer IFM-2026-00053 Work assignments This PhD position focuses on methodological and computational development in cryo-electron microscopy (cryo-EM), with emphasis on image reconstruction