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Posting Title Researcher II/III: EMT Modeling, Simulation and Analysis for Large-Scale Power Systems . Location CO - Golden . Position Type Regular . Hours Per Week 40 . Working at NREL The National
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challenges associated with capturing CO₂ directly from ambient air and is suited for candidates interested in combining computational modeling with experimental electrochemistry to develop energy-efficient e
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of the finite element method (modelling assumptions, boundary conditions, mesh/element choices, convergence checks). Experience with at least one FE tool such as Abaqus or similar. Scientific programming skills
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these challenges by advancing sensitivity-based modelling, fluid–structure interaction (FSI) methods, inverse problem solving, and surrogate modeling techniques, ultimately enabling predictive, adaptive, and
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candidates within Modelling Strength and Failure in Recycled AluminiumAlloys funded through the Centre for Research-based Innovation SFI FAST – Future Aluminium Structures. The positions are linked
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detection, and power grid waveform analytics. You will apply methods traditionally used in wireless communications and signal intelligence — including time-frequency analysis, interference modeling, multi
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modeling, environmental simulations, and digital twin systems. -Develop analytical tools, visualization platforms, and decision-support dashboards to improve research outcomes. -Ensure data quality through
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 11 days ago
Europe Research and Innovation Programme under the Grant Agreement No.101178775 Workplan: Design, develop and implement deep learning methodologies for generation of subsurface earth models. Duration
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mechanisms and kinetics to stabilize highly active but metastable surface motifs sustainable catalytic processes. Modeling Atomic Processes on Nanoparticles Develop atomistic models and machine-learning