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Field
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. Required competencies: Strong background in bioinformatics (e.g., R, Linux, Python). Experience working with large cohorts and high-dimensional data. Experience with microbiome analysis and/or GWAS
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, conduct experiment, and deploy Large Language Models on high-performance cloud Linux servers; and (f) assist in summarising and writing up research findings. Qualifications Applicants should: (a
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bioinformatics, computational biology, or a related field. Proficiency with Linux operating systems is essential, as is the ability to analyse and interpret large datasets and apply critical evaluation to current
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before the fellowship decision date. Documented experience in research projects within the scope of the awarded degree. Required competencies: Strong background in bioinformatics (e.g., R, Linux, Python
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doctoral degree, in the scientific area of Electrical and Computer Engineering [JB2.1][JB3.1]knowledge of generative neural networks, C++ and Python programming, able to use Docker container platform, Linux
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datasets, specifically in the areas of RNA-seq and/or pan-genomic analysis. Proficiency in the R or Python programming languages, familiarity in Bioconductor packages and Linux command line. Informal
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gene identification (MEGARes, CARD, ResFinder). Proficiency in Python, R, or Perl, with experience in Linux/Unix environments. Solid understanding of antimicrobial resistance mechanisms, horizontal gene
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of the projects. Basic knowledge in coding language, including but not limited to, basics of Linux, MATLAB, Python. Ability to carry out both wet-lab work for sample preparation and dry-lab work for image analysis
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groups/populations. Preferred Qualifications PREFERRED QUALIFICATIONS: 1. Demonstrated experience with Linux/Unix environment, Python, and PyTorch. 2. Demonstrated experience with programmable network
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the following languages: Python, R, or C/C++ and proficiency in use of Linux/Unix command-line system • possess a working knowledge on genomic sciences and sequencing biotechnologies • lead or