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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 10 days ago
reusable libraries in Python, Scala, and/or C++, taking advantage of Spark, Hadoop, and other tool stacks as appropriate. * Developing computational and algorithmic approaches to understanding
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be completed. Preferred Qualifications Expertise in Bash, Python, and R Experience with bacterial genomics, particularly with SPAdes, Prokka, Roary, RaxML, Kraken, breseq, and iTOL. Ability to work
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Theory). Advanced programming skills in Python, C++, and MATLAB. Excellent written and oral communication skills, with a proven track record of peer-reviewed publications. Work Location: Main Campus
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communication, political behavior, political psychology, or a closely related area. An affinity to computational tools and methods, and strong programming skills (e.g., Python and/or R). Familiarity with research
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. Experience in writing successful grant applications. Knowledge, Skills, and Abilities: Knowledge of wireless communication protocols and standards. Proficiency in programming languages, such as Python, C
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deep learning including data collection, architecture development, model training, and validation Interest in software development, with particular emphasis on the Python programming language and
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contributions. Demonstrated experience implementing, training, evaluating, or fine-tuning modern machine learning models. Strong programming skills in Python and experience building and maintaining research code
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selectivity, spin-polarized electron transport. • Skilled in the use of SEM, TEM and spectroscopy for nanomaterial, and thin film characterization. • Proficiency in Python, MATLAB, OriginPro, R or a similar
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, functional genomics, cell biology, and cardiovascular physiology. Experience or interest in programming languages such as R or Python. Independence to lead a project as well as willingness to work in a team
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experience with deep learning (DL) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets. Experience with sequence