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Field
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, SAS or Python. Excellent written and verbal communication skills. Experience analyzing real-world health, clinical, or biomedical data. Demonstrated ability to work independently and collaboratively in
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(Perl or Python, R, Linux tools, bash scripting) Experience in presenting at conferences Excellent publication record Strong motivation and dedication, scientific writing skills, problem-solving skills
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. Demonstrated expertise in writing advanced software in Python. Demonstrated experience with the LINUX operating system, LaTeX, Git, Python. Demonstrated expertise in the design and implementation of deep
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preferred; experience conducting original scientific research as well as publishing in scientific journals; demonstrated skills in scientific programming (Python preferred), workflow automation, and/or
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subject by the start date of the appointment Good programming skills (r, Fortran, Python) Familiarity with eco-evolutionary optimality approaches to modelling vegetation Experience with working with large
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Documented expertise in developing and training machine learning models (ideally with a focus on LLM), high-performance computing, data management, and software architecture Strong Python programming skills
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transcriptomics data using R/Python. Wet lab experience is beneficial but not essential. You have a strong enthusiasm for fundamental research and a scientific interest in the molecular genetics of immune cell
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Qualifications Ph.D. in Bioinformatics, Computational Biology, Systems Biology, or a related field Proven experience in the analysis of single-cell or spatial omics datasets Strong programming skills in Python and
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Python or MATLAB is considered mandatory, knowledge of Java is considered an asset Solid knowledge of transportation modelling, proved with indexed publications Experience with research projects and
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, engineering, computational science, a physical science (materials science, chemistry, physics etc.), or related field. Hands-on experience with AI frameworks and employing large language models. Strong Python