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Field
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-agent reinforcement learning (MARL) framework for cyber-physical networked fault-tolerant control of renewable energy-fed smart grids under adversarial conditions [6]-[9]. Multiple autonomous agents will
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identification context, while promising for network-level monitoring, has been largely underexplored. To this end, the project will explore the application of the next generation of deep learning algorithms, e.g
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requiring long-term strategies of building trust to gain access to the object of research. Fieldwork may consist of deep immersion in one place or research in a number of sites – in either case, the onus is
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, willingness to learn, and the ability to think creatively about complex physical systems are just as important as specific technical expertise. This PhD project—High-Fidelity Simulations of Geological CO2
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-efficiency trade-offs, using automated configuration to find Pareto-optimal designs under real deployment constraints. 2) Build the distributed learning loop. Develop the learning and update mechanisms
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in VR, deep learning, computer simulation, or psychophysics are another advantage. Specific RequirementsThe position is designed to encourage intersectoral and international collaboration and two
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with low-data, sparse, or noisy datasets, typical in early-stage drug discovery. Technical skills: Proficiency in Python (required). Practical experience with machine learning or deep learning workflows
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Machine Learning A PhD position is available at the Computer Vision Center (CVC) under the supervision of Fernando Vilariño and Paula García . The successful candidate will be enrolled in
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areas, could include deep learning (e.g. Long Short Term Memory - LSTM), statistical baselines (e.g. Autoregressive Integrated Moving Average - ARIMA, Kalman filters) and transformers (e.g., spatio
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), machine learning (ML), deep learning (DL) and Data science methods for medical image analysis, to autonomously grade the fundus images from large datasets. This will be supported by Professor Neil Vaughan