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the catalyst’s dynamic evolution. The goal is to select model systems based on the complex reaction networks involved in the CO2-to-hydrocarbons process, using machine-learned models for a consistent
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machine learning for vision. Hands-on experience with image acquisitions and different types of cameras (visible, infrared, RGB-D, etc.) is highly valued. You can demonstrate excellent study results. Your
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), user interface design, or data visualization techniques. Familiarity with frameworks for explainable machine learning (e.g., SHAP, LIME, Captum, Alibi). Experience in designing context-aware, adaptive
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tools Computer science » Systems design Engineering » Chemical engineering Engineering » Computer engineering Engineering » Design engineering Engineering » Industrial engineering Engineering » Materials
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representations in non-associative and associative learning and delineate the pathways and neuromodulatory systems underlying novelty-evoked exploratory behaviors. The research should integrate cutting-edge
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interdisciplinary research project at IDLab-MEDIA (https://media.idlab.ugent.be/ ), UGent – imec, aimed at advancing the state of the art in motion capture, sensor fusion, immersive media, and 3D computer vision
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, trustworthy, and fully explainable. The project introduces Generative Learning Cognitive Services (GLCS), intelligent, modular CPS components combining generative eco-cognition, cognition-oriented proactivity
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computational and machine learning approaches, you will decipher genomic regulatory programs and infer the evolutionary patterns of gene regulatory networks in cortical neurons, study their developmental origin
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in an academic environment for a 4 years period in view of a PhD degree. • You have a strong background in wireless and mobile networks and machine learning • You have excellent coding skills; hands
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physical principles into the learning process to maintain physical consistency outside the training domain. This PhD research is envisioned to result in a breakthrough in the application of machine learning