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Field
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learning-assisted computational pipeline for the automated detection of point defects in atomic-resolution scanning transmission electron microscopy (STEM) images. Using monolayer MoS₂ as a model system, the
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testing Perform numerical modelling and simulation, such as finite element modelling, parametric studies, and calibration against test data. Analyse experimental and numerical results, and prepare technical
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for the production of complex porous structures B.CAD modelling and CFD simulation of TPMS structures and other geometries applicable to chromatography. C.Production and physical, morphological and
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to attach when applying: Curriculum Vitae, duly updated, dated, and signed; Copy of academic degree certificates. If the possession of a degree is a requirement for the grant, candidates holding a degree
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curricular evaluation purposes, proven skills in programming, preferably Python; machine learning and artificial intelligence; language models and retrieval-augmented generation (RAG); ASR/TTS; diarisation
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supporting documentation, proven experience in all of the following areas: Computer vision and video processing (ingestion, ROI, 2D/3D keypoints, heatmaps); Deep learning and temporal modelling (CNNs
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harvesting using advanced metal organic framework material for operation in Kingdom of Saudi Arabia (KSA). This will be accomplished through the advance thermal and mechanical simulation of adsorption systems
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contributions to dark matter research. Collaboration members contribute to detector calibration, data acquisition, event reconstruction, background modeling, statistical data analysis, and Monte Carlo simulations
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dynamics (CFD) and/or multi-physics modelling using COMSOL Multiphysics, ANSYS Fluent, or equivalent simulation platforms. Hands-on experience in materials characterisation techniques including
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and modeling techniques that bridge the gap between laboratory-scale physics and field-scale implementation. Our mission centers on developing next-generation solutions for energy security, efficiency