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Field
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opportunity Experience in machine learning and deep learning Proficiency in one or more high-level programming languages such as Python, Java, or R. Strong commitment to interdisciplinary research and a track
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at the Department of Computer and Information Science will focus on the usability and interaction with semantic technologies. Based on the semantic web technology stack for implementing DPPs, and an ontology-based
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-driven energy systems research – scalable, transparent, and interoperable. Your tasks in detail: Development of a structured description format for the unambiguous and machine-readable characterization
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This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites Research Groups at the Faculty of Engineering, which conduct cutting-edge research
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vision systems, on the consideration of strong constraints on processing times and on the use of machine learning techniques in specific contexts (e.g. embedded targets, little data or explainable AI
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approaches that combine artificial intelligence, machine learning, natural language processing, and social sciences. This collaborative and cross-sectoral approach aims to produce robust methods for evaluating
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-flexible technologies, and integrate 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
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research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet
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knowledge of wireless communications, and signal processing. You have at least intermediary knowledge of machine learning algorithms, including federated learning, split learning, and graph neural networks
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bio materials and porous materials PhD student candidate 2 with background in computer science, AI, machine learning or related fields with the experience in CFD, ANSYS, COMSOL The successful candidates