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to staff position within a Research Infrastructure? No Offer Description The project explores extreme light–matter interactions at the nanoscale, developing advanced theoretical models in direct
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Engineering, Machine Learning, Applied Mathematics, or a related field. A strong academic background and interest in AI systems, embedded intelligence, edge computing, machine learning, or related areas. Strong
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motivation Testing whether the system performs equitably across cultural and language groups Training spans intervention design, trial methodology, human-computer interaction, and applied machine learning
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modelling, machine learning, or microfluidics. They will also have excellent communication, organisational and problem-solving skills, and a strong interest in interdisciplinary quantitative biology
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 2 days ago
pathways in cellular and mouse models of metabolic diseases You will perform biochemical and cell biological characterization of protein localization and organelle interactions You will apply and further
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theoretical modelling. The project will focus on self-learning active mechanical networks, but will be tailored to align with the interests and expertise of the successful candidate - we will mutually ensure
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learning-based surrogates for physical systems LLMs and scientific agents – large language models that autonomously reason, plan and execute scientific workflows AI for engineering design – LLM-driven agents
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; • mathematical modelling, development of FEM models (preferably using Ansys), and simulations, particularly in the areas of electromagnetics, power losses, etc.; evaluation, verification, and optimization
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Inria, the French national research institute for the digital sciences | Sophia Antipolis, Provence Alpes Cote d Azur | France | 3 months ago
and broaden the scope of geophysical models. We will develop a neural solver that dynamically learns to solve the momentum conservation equations. Unlike traditional supervised learning approaches, our
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, machine learning, explainable artificial intelligence (XAI), digital twins, and integrated data-model approaches. • Study of the frugality of the developed approaches by reducing the requirements