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disorder. This project investigates early neural markers of psychosis by integrating multimodal neuroimaging with genetic and transcriptomic data and applying machine-learning approaches to identify
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of this PhD is to develop physics-informed neural operator frameworks that embed governing equations and invariants of fluid mechanics directly into learning architectures, enabling real-time, generalizable
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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Intelligence and machine-learning approaches and emerging digital technologies such as non-contact sensors, smartphones, and computer tablets. This theme could also include research in data analytics and
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of Vienna). About the position: Lead the research group focusing on hybrid quantum algorithms, quantum neuromorphic computing, and quantum machine learning Build your own team (PhD students, postdocs) 4-year
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leave and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Position Description Advertised Job Summary The
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About the Opportunity The Lecturer will teach introductory courses in architectural drawing, sketching, studio design, computer modeling, architectural history, technology, or project case studies
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different environments influence behaviour and wellbeing. Advanced analytics, including AI and machine learning, will be used to interpret behavioural and emotional data, enabling real-time insights
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, organised researcher who can evidence: A PhD, or equivalent in statistics, machine learning or a closely related discipline, OR near to completion of a PhD. Expert knowledge of statistical inference methods
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, G. et al. Machine learning and wearable sensors for automated Parkinson’s disease diagnosis aid: a systematic review. J Neurol 271, 6452–6470 (2024). https://doi.org/10.1007/s00415-024-12611-x Nayan