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Field
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materials and energy-intensive manufacturing processes. These machines dominate current electric powertrain solutions but pose challenges in terms of resource supply chain, recyclability, and embedded carbon
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management skills. Candidates with laboratory skills and imaging are desired for this project. Must be eligible to enrol in PhD programs at Curtin. Application process Please send your CV, academic transcripts
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Design and conduct analyses linking vocal biomarkers to validated psychological scales (e.g., PAID, PHQ-9, GAD-7). Apply natural language processing (NLP) and AI techniques to large-scale audio datasets
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PhD Studentship: Towards Sustainable Powertrains: Enabling High Motor Performance without Rare Earth Materials and Energy intensive Manufacturing Processes The University of Nottingham This project
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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
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, social sciences, and physical sciences to address real-world challenges. Faculty and students conceptualize, prototype and evaluate products and processes that enhance human potential, well-being and
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array antenna systems for imaging MIMO radar in autonomous driving applications. This work will advance the design and characterization of intelligent devices and environments for wireless communications
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document fossil turtle specimens Process and analyze digital CT datasets of cranial and neurological structures Carry out geometric morphometric analyses to study shape variation Conduct eco-morphological
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for or against AI in different contexts. The successful candidate will be a member of an active research group at Østfold University College and is expected to do research, develop prototype implementations, and
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of electron microscopy imaging and spectroscopy to reveal the structure–property relationships that govern molecular adsorption mechanisms. This interdisciplinary project is fully funded by DTU’s PhD grant