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, and programming. Knowledge and experience in artificial intelligence and machine learning is expected, but not required. Knowledge and experience in deep learning and generative AI is considered
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electromagnetic device performance analysis>>>Embedded SystemsResearch focuses on:•Robotics, computer vision, and machine learning/deep learning•Wearable and implantable sensors, biosensors, and tele
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thermodynamics, with an emphasis on both theoretical and practical applications. Experience in machine learning and AI, particularly deep learning frameworks such as TensorFlow, and their application in fluid
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-informed deep learning is rapidly advancing, integrating artificial intelligence with the governing physical laws to achieve more faithful representations of atmospheric processes. In the field of remote
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Engineering). - Theoretical foundations of 6G RAN and autonomous systems o Proven knowledge of AI-native RAN systems. Indicative skills/experience: - Deep understanding of 5G/6G RAN architecture (O-RAN, NG-RAN
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prioritization. By explicitly combining ecology, economics, and decision theory, EcoDisco seeks to produce methods that are robust, policy-relevant, and sensitive to the deep uncertainties inherent in conservation
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using deep learning and AI-driven image analysis. You will: - Analyse pre-implantation kidney biopsies according to the Banff criteria; - Apply AI methods for automatic segmentation and morphometry
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scienceEducation LevelMaster Degree or equivalent Skills/Qualifications 1. Planning and conducting computational experiments on deep learning models for small molecules; 2. Implementing novel training techniques and
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) To develop Deep Learning algorithms to significantly speed up probabilistic inference algorithms of current spatial birth-death models 2) To incorporate fossil stratigraphic and spatial information into a new
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. The research will combine computational modeling (e.g., NLP, machine learning, deep learning) with human-centered research (e.g., user studies, experimental design, qualitative analysis). We are looking not only