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candidate will be supervised by P.M. Congedo, E. Denimal Goy and Olivier Le Maître, experts in uncertainty quantification methods. The work will be conducted in the Platon team, a joint research group between
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allocation and system performance under uncertainty. This includes formulating complex decision problems, designing deterministic and real‑time solution methods, and contributing to high‑quality academic
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optimization-based network partitioning point to scalable, communication-aware control designs; stochastic MPC and co-design studies demonstrate methods for handling uncertainty and jointly optimizing assets and
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, digital twins) Familiarity with process control, optimization, and uncertainty quantification Experience integrating simulation (e.g., CFD, DFT, kMC) with machine learning models Background in energy
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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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systems and nuclear facilities. These environments present fundamental challenges: limited sensing, high uncertainty, and complex physical interactions. This project addresses one of the central challenges
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picking performance (e.g., cycle time, pose uncertainty, etc.). ? Collect data from various 3D imaging systems. ? Design and develop 3D printed artifacts and optimize 3D printer parameters for improved
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Design Lab – works on modelling, control and optimization for mechatronic systems, industrial robots and processes (https://dynamics.ugent.be). We are part of the department of Electromechanical, Systems
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the long-term performance of water reservoir systems, exacerbating uncertainty in both water availability and demand. This research programme aims to advance reservoir operation and planning under deep
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in developing methods to quantify uncertainty