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Field
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tools such as R, Python, or MATLAB as well as relevant machine learning frameworks Experience in statistical data analysis, and expertise in areas such as experimental design, linear/nonlinear models
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. Desirable: Proficiency in scientific programming (e.g. Python) and familiarity with data science and machine learning techniques. Experience with geochemical analytical techniques and working in a laboratory
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journals. Have experience in: (i) integrating single-cell and spatial multi-omics; (ii) computational programming in R, Python (and other common computer languages); (iii) competence/interest in analysing
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mathematics, signal/image procesing, machinelearning/deep learning and computer science. It will be an advantage to have: Scientific programming proficiency in Python, knowledge of deep learning libraries in
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in agricultural soil microbiology. The participant will also have active exposure to statistical data analytics using R, Python, and current bioinformatic software. The participant will gain or enhance
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have been received within the past five years. Preferred skills: Experience with ecological modeling, spatial analysis and statistics, ArcGIS Pro software, Python and R coding. Stipend $60,000.00
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Python, R, or similar Excellent analytical and problem-solving skills Ability to work both independently and collaboratively in a team Previous research experience and publication record in a related field
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and interpersonal skills. • Possess relevant hands-on experience in Python, Java, C++ etc. and corresponding development environments.
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bachelor’s degree in Economics or a related field by the start date. Good knowledge of Stata and/or Python is preferred. For further information regarding the skills required for this role please see the
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and skill in programming with MATLAB or Python. Research experience in MEG, in vivo electrophysiology, in vivo two-photon/miniscope imaging, slice electrophysiology, and mouse brain surgery is desired