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interfaces. The structure-activity relationship between groups in polymers, metal centres and electrochemical CO2 reduction/evolution performances will be studied by FTIR, NMR, CO2 adsorption/desorption, TEM
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now Fatigue profoundly impacts mobility and quality of life in people with neurological disorders, yet its effects on everyday movement remain poorly understood. In this PhD project, you will combine
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field of research Participate in education, and PhD and master student supervision Profile Strong background in computational biology, bioinformatics, machine learning, or a related quantitative field
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(UTC) Country Czech Republic Type of Contract To be defined Job Status Full-time Hours Per Week To be defined Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
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process and regulatory environment relevant to the EMBRACE programme Experience using User Experience (UX) and User Interface (UI) to create digital products that are both functional and visually appealing
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) Established Researcher (R3) Application Deadline 21 Oct 2026 - 00:00 (UTC) Country Singapore Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not
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multiple categories of technical hardware, such as audiovisual equipment, sensors, networking devices, computers, lighting systems, or control interfaces. Specific Requirements Essential skills: Practical
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Language Processing, stream analytics, IoT infrastructures, and agricultural robotics can be combined to create human-in-the-loop conversational interfaces for smart agriculture. The goal is to allow farmers
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
managerial insights and practical guidelines for practitioners. Take part in the mandatory PhD research education program Conduct independent research and publish the result in recognised scientific journals
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or application-specific computational workflows. The PhD project aims to address these limitations by developing a unified, scalable, and interpretable framework for probabilistic unsupervised learning