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expertise in object-oriented programming in Python, data modeling, and scientific workflows experience in collaborative Linux-based development using Git knowledge of Python-based type systems and schemas
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services can be found at: https://www.uu.se/forskning/snpseq and https://ngisweden.scilifelab.se/ We are proud to deliver high-quality data and are accredited by SWEDAC as a testing laboratory under the ISO
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etc., please see: https://csrankings.org/#/indexai&vision&mlmining&nlp&robotics&bio&world and https://mbzuai.ac.ae/study/faculty-directory/ They will work on developing Artificial Intelligence (AI) and
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Fondation Nationale des Sciences Politiques | Paris La Defense, le de France | France | about 1 hour ago
of the most recent econometric methods to study the evolution of wealth accumulation from incomplete tax data. The applicant needs to have advanced programming skills in Stata, R and Python
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Appointment Term: 2 years Appointment Start Date: July 1, 2026 Group or Departmental Website: https://ed.stanford.edu/faculty/ksadow (link is external) How to Submit Application Materials: Fill out
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of machine learning or quantum computing Programming skills in Python and/or C/C++ Experience with scientific software tools and numerical libraries Familiarity with Linux-based computing environments Ability
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Qualification: • master's in Computer Science, Mathematics, Physics, or related fields; • experience with some programming language such as Python, Matlab or Julia; • excellent analytical and problem-solving
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skills. Is confident using digital learning tools such as Python, SAS, Stata or similar. Holds (or will soon complete) a doctorate in Finance or a closely related field. Enjoys contributing to the wider
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. Practical experience applying machine learning or deep learning methods to biological data. Proficient in Python, with working knowledge of bash and experience using HPC or cluster environments (e.g. SLURM
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experiment on a metric-scale concrete wall, using the multi-physics NDE probe integrated onto drone platforms. Expected Results A Python-based multi-physics Maching Learning framework for NDE characterization