38 software-engineering-model-driven-engineering-phd-position Postdoctoral positions in Hong Kong
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experience in one or more of the following areas: development of methods for multi-omics data integration, application of machine learning models in life science, single cell data analysis and spatial
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be requested to arrange for at least three references, including one from their PhD supervisor/advisor. Review of applications will commence as soon as possible and continue until November 30, 2025
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Biology, Immunology, or a related discipline. Experience in cancer research using various animal tumor models would be an advantage. The appointee will work together with a research and development team
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on protein modeling and de-novo protein design. A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits. The University
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of renewal subject to satisfactory performance. Applicants should have a PhD degree in biological/biomedical sciences or a related discipline. They should be hardworking, self-motivated, and able to work
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in longitudinal analyses, multilevel modelling, data visualisation, and state-of-the-art statistical and epidemiological models would be an advantage. The appointees will be primarily responsible
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(e.g., multilevel modelling, structural equation modelling, latent profile analysis, etc.), will be an advantage, as will a strong publication record with internationally recognised journals
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Chinese (knowledge of spoken Cantonese would be an advantage). A strong background on quantitative research methods and statistical modeling as well as the design and validation of performance assessment
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of responsibility and commitment. Preference will be given to those with previous research experience in virus-host interaction, influenza viruses and animal models. Eligibility to work in Biosafety
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computational screening, or machine learning for materials property prediction is essential. Candidates with prior experience in developing AI models for accelerated materials discovery, optimization of material