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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
ability to program (e.g, python, R, and/or javascript). Must have at least some experience working with geospatial data. Preferred Qualifications, Competencies, and Experience Ability to develop impactful
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(Jupyter / Marimo), Git (or equivalent VCS). Programming Languages – Python, R (optional). Frameworks – PyTorch, TensorFlow (optional), MLFlow (optional). Databases – SQL (including queries using windows
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electron microscopy and fatigue testing of metals Skills in in situ testing, FIB sample preparation, and image/data analysis tools (e.g., DigitalMicrograph, ImageJ, Python/Matlab) are beneficial. Strong
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) Proficiency in programming languages such as Python or C++ Experience with AI frameworks like PyTorch or TensorFlow Strong communication skills and ability to work in a team environment Ability to model
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programming in, for example, R or Python. Particularly valuable is a research background in ecology, biodiversity, systems biology, or related areas, as well as experience working with time-series data, dynamic
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, preferably in Python, Fortran, Matlab or R #experience in the environment of High Performance Computing (HPC) is desirable, but not mandatory #capability to work in a team but able to formulate and carry out
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skills in statistical software (e.g. R, Stata, Python) and working knowledge in SQL Excellent written and oral communication skills Strong record of distinguished scholarly achievement, including written
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-Scripting very good knowledge in programming, preferably in Python,Fortran, Matlab or R experience in the environment of High Performance Computing (HPC) is desirable, but not mandatory capability to work in
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. Proficiency in computer programming, including scripting in Python, Fortran, or other computing tools for data processing and modeling. Experience in grant proposal writing within a collaborative environment
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reaction. Expertise on data analysis with R or Python tools. Experience in processing of targeted and untargeted mass spectrometry datasets, ideally in the field of lipidomics or other omics sciences