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Institut de Recherche pour le Développement (IRD) | Montpellier, Languedoc Roussillon | France | 5 days ago
these datasets, build models of carbon stock dynamics in ecosystems. Your work will contribute specifically to objective (2), focusing on quantitative IR spectroscopy. Over the past thirty years, many research
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. Robust reservoir-scale modelling is therefore essential for predicting system performance and informing design and operational decisions. Leveraging geological, hydrological, and thermal models developed
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I supervise projects in particle physics. My main emphasis is on phenomenology, comparison of predictions with experimental measurements. I follow developments in flavour physics: weak decays
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validate the concept. This includes: - Translation of simulation insights into reactor design - Experimental investigation of particle retention and transport - Comparison between model predictions and
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: formulate and analyze stochastic models of evolving populations using methods from statistical physics, applied probability, and population genetics; develop inference frameworks that link model predictions
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applied mathematical modelling machine learning multi-fidelity modelling numerical methods. Demonstrated programming ability (MATLAB/Python/C++) and enthusiasm to learn PyTorch. Previous experience in one
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statistical physics, applied probability, and population genetics; develop inference frameworks that link model predictions to genomic and epidemiological data; design controlled computational experiments
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failure (if they can), but they cannot explain why it happened or calculate the exact contribution of each individual stressor. Furthermore, these models often fail and make overconfident predictions when
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of energy and computational power. Reservoir computing offers a promising alternative by using complex physical systems to perform tasks such as prediction, classification, and signal processing. However, one
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project focuses on environmental sustainability in grain storage, developing telemetric robotic sensing and predictive modelling to control mycotoxin (Ochratoxin A) risk while reducing postharvest drying