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in autonomous systems such as ground and aerial vehicles, and mobile robots. This includes: formulating and solving long-standing multiterminal information theory problems using modern machine learning
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for secure and resilient systems; privacy; secure sensing and control in critical infrastructures) Candidates with a PhD in Electrical Engineering, Computer Engineering, Computer Science, or other closely
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with the CDT’s aim to achieve a sustainable wind farm lifecycle by developing methods for high-value reuse of composite turbine blades. Machine learning and non-destructive evaluation techniques will be
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, biodiversity monitoring, and climate resilience. The work supports strategic priorities in Environmental Sciences, Software/Cyber. PhD researchers will explore how AI-driven Earth observation, computer vision
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or equivalent Skills/Qualifications Technical Skills: Programming and integration of machine learning algorithms, reinforcement learning and symbolic planning in real robotic platforms. User modeling techniques
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applications. The project integrates: Computational Fluid Dynamics (CFD) and multiphase flow modeling Radiative heat transfer Machine learning and reduced-order modeling Data-driven optimization for industrial
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in Estonia and Europe through competitive research grant applications, especially as a coordinator. Involving students (BSc, MSc, PhD) in research and development projects and supervising bachelor's
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. Into the second year, the project moves toward methodology refinement and Machine Learning integration. The student will execute a more ambitious cycle with a complex alloy system and integrate machine learning
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minimizing computational and energy costs. The proposed approaches will rely on machine learning methods applied to image analysis, with the objective of enabling early identification of at risk areas and
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | about 1 month ago
-scale (~10’s of km2) permafrost thermo-hydrological hybrid twin, to be coupled with state-of-the-art freezing/thawing soil mechanics machine learning-based surrogate models (Richa et al., 2024, Tristani