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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 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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Technology » Computer technology Technology » Communication technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 4 Oct 2026 - 23:59 (Europe/Oslo) Country
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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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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
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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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the suitability and potential benefits of LLMs (and other machine learning models) in these tasks. These investigations include the feasibility, practicality and success evaluation of prototype implementations
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Fundacio de Recerca Clinic Barcelona-Institut d'Investigacions Biomediques August Pi i Sunyer | Spain | 8 days ago
or ELISA. Scientific curiosity, eagerness to learn, and strong teamwork skills will be highly valued. Research group: Cellular Immunotherapies for cancer, Sònia Guedan Project: Next-Generation CAR-T Cells
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numerical simulations and machine learning in order to better understand and characterize active matter systems. A possible direction is to use physics-informed machine learning techniques to connect
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candidate will investigate novel sensing methodologies, develop physics-informed computational models, and apply state-of-the-art machine learning techniques to extract meaningful information from complex
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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