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applied statistics, data science, machine learning, text analysis, and familiarity with coding in R and/or Python. All applications must be submitted through Columbia University?s Academic Search and
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languages (e.g., R, SAS, Python). Working Conditions Office-based. Application Documents Resume (required) When applying, the document(s) MUST be uploaded via the My Experience page, in the section titled
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Independent way of working Basic programming skills (Python) advantageous Very good knowledge of German or English (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements ), ideally
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 17 hours ago
), with evidence in open-source code or deployed tools. - Solid programming and ML/NLP engineering skills in Python or ideally modern deep-learning stacks (e.g., PyTorch/JAX, HuggingFace/Transformers
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research involving speech, voice, or motor control Experience managing a team of student researchers Technical aptitude in any of the following: MATLAB, Python, R, signal processing, or biomechanics Other
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and experience. Knowledge of scientific programming languages such as C/C++, Fortran, Python, R, etc. Working knowledge of software related to cluster computing and servers, such as Flex, Slurm, PBS
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of computational electromagnetics. High proficiency in Python, MATLAB, Wolfram Mathematica, or similar programming software. High motivation and creativity. Good written and verbal communication, including
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Python, MATLAB, Wolfram Mathematica, or similar programming software. High motivation and creativity. Good written and verbal communication, including presentation skills. Good organizational skills
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fluency in at least one programming language (e.g. Python, R, or similar). Background in cancer biology preferred Problem Solving Works with a team to troubleshoot computational analysis Decision Making
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Python or C/C++. The candidate should have an interest in developing novel bivariate methods in machine learning for molecular property prediction within an interdisciplinary application. Ideally