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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
vision or video understanding; Multimodal learning; Large language models. Programming experience in Python and familiarity with deep learning frameworks such as PyTorch are expected. Knowledge of sign
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backgrounds such as AI, computer vision, computer graphics, machine learning, robotics, wearable technologies, textile engineering, fashion technology, digital fashion, or related areas are encouraged to apply
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
. The successful candidate will be expected to carry out the following scientific activities: Application of advanced Machine Learning, Deep Learning, reduced-order modelling, and physics-informed/-guided modelling
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machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in industrial furnaces
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Machine Learning, Reinforcement Learning, AI-based time-series forecasting English language skills, both written and spoken, corresponding to the scale C1 in the Common European Framework of Reference
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, Mathematics, Engineering or a related discipline, with an interest in AI applied to complex and safety-critical systems. Good knowledge of Machine Learning and Deep Learning methods, including experience with
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chemistry and study the physicochemical properties of peptides loaded into the materials. Build surrogate models and apply machine learning techniques to extract design rules and rapidly screen thousands
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on the field can be derived from first principles, and how these constraints can improve the technique's performance, particularly when embedded in modern machine learning models. The ultimate goal is to
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machine learning, embedded systems, and edge intelligence Hands-on experience on Nvidia Jetson boards or other edge platforms Strong knowledge in deep learning, particularly large language models or multi
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process data locally while ensuring efficient and scalable artificial intelligence at the edge. TinyML and Edge AI have demonstrated the feasibility of embedding machine learning models on such devices