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of Physics (Ref.: 532531). Applicants should possess a Ph.D. degree in Condensed Matter Physics. Experience in numerical techniques and analytical field-theoretical approaches is desirable. Applicants who
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, and assessing the impact on disease progression. Develop relevant disease models to assess therapeutic efficacy of a specific treatment. Demonstrate understanding of the disease pathology for better
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Experience in image analysis packages such as Freesurfer, FSL, SPM, or 3DSlicer, or using machine learning or artificial intelligence models would be advantageous What We Offer The appointee would be exposed
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, molecular and cell biology, and animal models. Skills in bioinformatics are advantageous. Candidates should be highly motivated and have a track record of publications in international journals. The appointee
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plasticity with a focus on cancer stemness using hepatocellular carcinoma as a model system, that is part of a theme-based collaborative project. For further information, please contact Professor Stephanie Ma
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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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analysis, and proficiency in statistical and computer modelling software (e.g. R, Python, Matlab, and C++) would be advantageous. The appointee will work with a research team to study the methodologies
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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