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Field
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biology, machine learning, or structural bioinformatics Strong programming skills in Python and proficiency in Unix/Linux environments Demonstrated record of publications in peer-reviewed journals Excellent
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; knowledge of Linux-based development, software architecture, system integration, and reproducible research workflows; and ability to work in a multidisciplinary research environment involving engineers
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the closing date for applications Strong publication track record indicating ability to complete high-impact work Familiarity with Git and GitHub Competence using Linux-based high-performance parallel computing
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disciplines with strong interest in accelerator and FEL science will also be considered. Strong programming skills including: Proficiency in Python, Working knowledge of UNIX or Linux, Demonstrated background
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to molecular systems. - Familiarity with scientific programming and data analysis using languages such as Python or Julia, and experience with Linux and high-performance computing environments. - Strong written
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and/or Linux command line • Experience with DNA/RNA extraction and marker development • Experience working with large-scale datasets • Experience with plant phenotyping for disease resistance, including
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required. Knowledge, Skills and Attitudes: Proficiency in Python and/or R and in Linux shell scripting, with working knowledge of a workflow manager and containerization. Working knowledge of standard
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Qualifications PhD, Computational Biology, Bioinformatics, Computer Science Training in statistics, and programming experience in a UNIX/Linux environment using programming languages such as Python, R, and/or Perl
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, significant coding experience (both Python and C/C++ or P4), and a record of working in a Linux environment and related scripting languages. How to apply Applications for this vacancy are to be made online via
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, proficiency in programming languages such as Python or R, Linux/Unix command-line skills, and the ability to develop reproducible analytical workflows. Experience with infectious disease laboratory techniques