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) communication and networking technologies, embedded system design, and computer programming (C++, Python, etc.). Ability to work in an interdisciplinary team and interested in collaborating with industrial
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PhD Stipend in machine learning methods for the analysis of IoT time-series data. At the Technical Faculty of IT and Design, Department of Computer Science, one PhD stipend in machine learning
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University (Aalborg CREATE city campus) on the PhD program Media, Architecture & Design, and forms a central part of the externally funded BuddhAi project, which is an interdisciplinary AAU initiative
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PhD Studentship: Enhancing Material Properties and Longevity in Maxillofacial Reconstructive Devices
of working with polymers and materials characterisation. Knowledge or experience o f medical/dental devices or prosthetics and computer-aided design. How to apply Dr Keith Winwood (K.Winwood@mmu.ac.uk ) will
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. In addition to the Department of Architecture, Design, and Media Technology, the project involves close collaboration with the Department of Computer Science and is part of a larger project involving
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) Country Netherlands Type of Contract Temporary Job Status Not Applicable Hours Per Week 36.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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(Europe/Rome) Country Italy Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position
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student will apply AI‑based protein design approaches and leverage advanced computational tools to design small, custom‑made protein binders that can attach to VCP/p97 and alter its conformational dynamics
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Innovation in Construction and Civil Engineering, Monday to Friday 15:00 to 18:30 Tasks to be performed The National Plan Project PID2023-149956OB-I00 identifies the need to ‘hire a person to assist with data
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Join MultiD Analyses AB and the University of Gothenburg to develop innovative bioinformatics and machine learning methods for RNA Fragmentomics, with the ambition to improve cancer care through