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the timeline of project research outcomes, data collection, management, analysis and interpretation, preparing research reports and research papers, training research assistants, and performing other duties as
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in related fields; Degrees that combine innovative approaches with strong theoretical knowledge are particularly relevant; and Should demonstrate expertise in experimental methods, qualitative analysis
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quality assessment filters and standardise data augmentation. Machine Learning Model Development and Analysis Train and benchmark unimodal (image-only) and multimodal (image + questionnaire) models (e.g
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. Applicants should have a PhD degree with research experience related to neuroscience in learning and memory, or in sleep related research. Experience in EE/EMG analysis in strongly preferred. Experience in
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the research project - “MiR-17-92 members in IOP regulation”. He/She will be required to: (a) perform gene editing, RT-PCR, Western blot analysis, confocal microscopy and electrophysiological experiments; (b
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record of publications in relevant fields. Have proficiency in data processing and statistical analysis, with experience in programming languages/software such as MATLAB, Python, R, Stata, as well as GIS
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factor of the journal; (ii) journal rank in category in the format of ‘the rank out of total number of journals in that specific category’, with reference to Journal Citation Reports (JCR); and (iii
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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
innovative methods of assessment and/or advanced statistical methods, such as multiple linear regression, mediation analysis, multilevel modeling, and/or latent variable models. Experience in managing and
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biomedical sciences, bioinformatics, clinical research or related field strong clinical trial experience (design, execution, and regulatory submissions) proficiency in bioinformatics, statistical analysis (R
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in flow stability analysis and solid knowledge of computational fluid dynamics. Applicants are invited to contact Prof. J. Hao at telephone number 3400 8060 or via email at jiaao.hao@polyu.edu.hk