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integrating modeling, machine learning (ML), and advanced control methodologies. The research will focus on designing AI-driven algorithms to assess battery health, predict degradation trends, and optimize
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biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run high-performance numerical experiments
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties
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, AtomGPT). Working Knowledge Of: • Workflow tools (e.g., ASE) and HPC environments. • Software development in Python, Git-based version control, and Conda packaging. • Data integration and surrogate modeling
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unsupervised learning Distributed / decentralised command and control: synchronisation, coordination, adaptation, for example using multi-agent systems Decision support under uncertainty Modelling and simulation
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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | about 1 hour ago
Infrastructure? No Offer Description The aim of this PhD is to model the development of microstructures during welding processes on thick parts, in the context of nuclear equipment, for which deposits of several
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understanding of the underlying physical mechanisms and to leverage this knowledge to develop predictive tools for optimizing the design and control of wind farms. Research scope and responsibilities Depending
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, creating predictive models for failure control. Validation & Experimental Collaboration: Compare simulations with experiments, collaborate on proof-of-concept testing, and refine models based on results
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to generate baseline datasets for calibrating and validating predictive models of biodiversity-rich forests. Using machine learning (ML) algorithms, the Research Assistant will help predict the occurrence
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microfluidic analogs of phloem sieve plates and other plant hydraulic elements. Conduct controlled flow-pressure experiments to evaluate aspects of the theoretical predictions and quantify resistance mechanisms