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Field
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). Methods include sensor-based technologies, external and internal exposome approaches, and co-creation and participatory research methods. This PhD position seeks to contribute to and build upon work being
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of perceptual foundation models, using advances in deep machine learning and computer vision. The goal is to invent, develop and evaluate novel methods for pre-training and fine-tuning of perceptual foundation
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established techniques for quantum circuit representation and manipulation (e.g. diagrammatic methods like ZX-calculus) towards description of quantum noise employ such extended methods for quantum circuit
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existing methods to address such risks, and on how to achieve a better use and exchange of existing protocols and data. The project includes the use of Digital Twins to simulate cascading disaster effects
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edge applications. Tasks and responsibilities: Develop new deep learning, computer vision and multimodal learning methods for pre-training and fine-tuning perceptual foundation models. Actively
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responsibilities for the Research Assistants are: Learn psychophysiology data management and processing methods Implement data management, preparation, and processing methods with participant files with detail
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18 Sep 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering » Computer engineering Engineering » Electrical engineering Physics
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in developing new machine learning methods for multimodal data and
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methods in fluid dynamics and heat transfer to study multiphase flow phenomena. The goal is to integrate theoretical and experimental fluid dynamics with modern computational tools to analyze and predict
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human perception and cognition. Experience in: experimental methods, computer rendering or VR/XR, programming. Affinity with multidisciplinary work, combining science, art, and technology. Enthusiasm