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based on the selected hire’s education and experience. Special Instructions: A cover letter, resume, and contact information for three professional references (to include at least one previous supervisor
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into the ethical governance of Large Language Models (LLMs), as part of the prestigious Divirsibus Vis Plurima Solvo project. The position is full-time and fixed term for 41 months or to the funding end date of 30
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emerging autonomous vehicles. The whole project encompasses a broad spectrum of academic, industrial and societal partners and includes a large group or researchers. The postdoc position will advance state
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Stroke Research and Prevention Group of HKU (stroke.hku.hk) to assist in neuroimaging data acquisition and analysis, write-up of the results, as well as training of students Apply image analysis skills
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rules which enable effective learning in large and deep networks and is consistent with biological data on learning in the cortex. In particular, the research will focus on evaluating and extending a
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candidate with skills and experience in some of the following areas: Quantitative approaches such as longitudinal data analysis, analysis of large datasets and/or data science Experience of working with
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satisfactory performance and funding availability. Applicants should possess a Ph.D. degree in epidemiology, biostatistics, applied mathematics, data science or other related disciplines. They should have strong
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expertise in Neuropixels or other large-scale in vivo electrophysiology techniques. An expert neural data analyst may be considered even with minimal animal experience. Required Application Materials: Cover
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. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics for real-time battery health monitoring and fault detection. Collaborate with
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desirable. Strong analytical capabilities and proficiency in the quantitative analysis of large datasets are also required. Click the 'Apply' button below to register an account with our recruitment system