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Field
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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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Description The University of Wuppertal (Germany) invites applications for a PhD position (Research Assistant) in the group of Prof. Peter Zaspel, starting March 1, 2026. The position is part of
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IMT mines Alès, Mines Saint Etienne (50%/50%) | Ales, Languedoc Roussillon | France | about 13 hours ago
remains poorly understood. The objective of this PhD is to characterize and model the biomechanical consequences of menopausal tissue remodeling in five pelvic-related soft tissues: skin, fascia, muscle
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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | about 15 hours 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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, 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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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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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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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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, 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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comfort throughout the year in a Nordic climate? Is it possible to predict dynamic outdoor thermal comfort with sufficient accuracy using fast parametric algorithms and machine learning (ML) models instead