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networks for real-time, adaptive diagnosis. b) Uncertainty in Dynamic Environments: Runtime uncertainties require sophisticated risk modeling; we will employ Bayesian deep learning and deep reinforcement
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The Danish Center for Hadal Research (HADAL) at the Department of Biology, University of Southern Denmark, invites applicants for 5 Ph.D. positions in deep-sea biogeochemistry and microbial ecology
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 11 days ago
dynamics data and advanced graph-based deep learning models to decode long-range communication pathways within macromolecular complexes. The PhD candidate will play a central role in this effort by
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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep
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Experience with MATLAB/Simulink, including Control System Toolbox, System Identification Toolbox, or Deep Learning Toolbox. Understanding of battery systems, electrochemical energy storage, or battery
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-driven model selection, and deep learning for data analysis and feature extraction from characterisation data. Surrogate modelling will be employed to reduce computational costs, and AI-based uncertainty
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and spoken English. Desirable: Experience with photonic/electromagnetics design software. Familiarity with deep learning platforms (e.g. TensorFlow, PyTorch). Funding and eligibility The project is
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- Knowledge in programming in Python or R - Familiarity with machine learning or deep learning methods is a plus - Interest in plant genomics, evolutionary biology, or comparative genomics - Proficient in
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manufacturer and technology company. Applications are invited for a 3.5-year EPSRC funded UDLA PhD studentship. The studentship will start on 1st October 2026. Project Description As Deep Learning and
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latent coordinates; ii) Cross-modal alignment (e.g., canonical correlation analysis and Deep CCA) to align heterogeneous parameterizations and modalities in a shared latent space, iii) Operator learning