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Field
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candidate would become an ARIA R&D creator. Responsibilities: Conduct foundational research on aggregation mechanisms, including: for-mal modeling and axiomatic analysis, computational complexity and algo
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in Europe by fueling innovation through research partnerships with industry, boosting R&D investments leading to economic growth, and attracting highly qualified talent. We look for researchers from
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
: * Introductory proficiency in Python, R, or another programming language * Prior exposure to machine learning or AI techniques in clinical research * Experience using deep learning frameworks * Familiarity with
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competitiveness in GM. AMMIC brings together world leading research facilities and expertise to stimulate new investment in research and development (R&D) to address regional and national materials and
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genetics, and the ability to use R and/or UNIX/command line applications will be given preference. Pay and Benefits Pay Range: $58,656.00 - $61,008.00; depending on education/qualifications/experience Please
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of statistical programs (e.g. in Matlab, R or SPSS) German and English skills Proven ability to collaborate effectively, maintain performance under tight deadlines, and demonstrate a high level of commitment and
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knowledge of statistics (R) Experience with multiverse analysis and open science practices Experience in the measurement and evaluation of eye tracking data is desirable Experience with physiological data
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or disease modeling in R and/or Python statistics data analysis communication (written and verbal) This does not mean you need expertise in these areas but rather that you have some base knowledge upon which
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analysis software (e.g., SPSS, R, NVivo) is highly desirable. · Publication Record: Evidence of scholarly productivity, including publications in peer-reviewed journals. · Communication Skills: Strong
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Microsoft Word, Excel, Access and PowerPoint. Preferred Qualifications Proficiency and knowledge of multiple statistical software packages preferred, such as SPSS, SAS, R, Python, etc. Experience with ML/DL