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Ref.: 532531 Work type: Full-time Department: Department of Physics (25600) Categories: Senior Research Staff & Post-doctoral Fellow Post-doctoral Fellow in Computational and Theoretical Condensed
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Ref.: 532117 Work type: Full-time Department: Teaching and Learning Innovation Centre (47500) Categories: Senior Research Staff & Post-doctoral Fellow Applications are invited for appointment as
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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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to structural biology, protein engineering, machine learning, molecular cloning, in vivo experiments, and/or CRISPR technology. Candidates must exhibit a strong command of written and spoken English, and
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technical skills in next generation sequencing, including library preparation and bioinformatics analysis. Experience in developing bioinformatics pipeline and maintaining computer server would be
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, fluent in written and spoken English, and competent in basic computer database management. Familiarity or experience with gene sequencing or gene manipulation will be an advantage. The appointee will be
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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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informed neural networks (PINN) and explainable machine learning (EML) frameworks; experience in related technologies including large-scale data analysis, deep learning, Python, PyTorch; and the ability
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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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. Knowledge and skills in mathematics, biostatistics, or advanced statistical techniques in clinical research, database management, and machine learning (AI) will be taken into account. They should have