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Field
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change theory and practice, implementation science, and associated measurement and analytic techniques. The candidate is expected to help bridge these domains using validated statistical tools. Applicants
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informatics, biomedical engineering, statistics, or related fields. The lab is engaged in developing novel deep learning and AI-based technologies for digital biopsies from medical images and real-world
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statistical analyses to assess stability and plasticity of multisensory representations Collaborate with experimental partners and team members to interpret findings and develop brain-machine interface
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, STATA, SAS or other statistical packages; demonstrated expertise in the analysis of large and complex datasets; excellent written and oral communication skills in both English and Chinese (Cantonese and
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statistical models (for example principal component analysis) to obtain insights into relationships between physical properties of polysaccharides (composition, molecular weight charge, chain length etcetera
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economic studies funded by the UK National Institute for Health and Care Research. Experience of conducting economic evaluations using suitable statistical software (e.g. STATA, R or SAS) is essential
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. Applications by email will not be considered. Application procedure and conditions We kindly request applicants to provide their nationality for statistical purposes only, as part of our commitment to promoting
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skills: Experience with machine learning and statistics Knowledge on mutational mechanisms in cancer Publications as co-author or first author in a related area Fellowships, grants and prizes Place of
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modified RNA and DNA molecules Supervise Sophie Davis and/or CCNY students in bench work and laboratory techniques Collect, record, and analyze experimental data using basic statistical methods Collaborate
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capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives, including large-scale