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to help with participant recruitment Verify participant data daily to ensure collection is accurate and process data with MATLAB, Python, and R scripts as needed Prepare and maintain IRB materials, ensuring
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)/machine learning (ML) applications in the power grid. * Experience with power system modeling, simulation, dynamics and/or optimization, phasor and electromagnetic transient (EMT)-based modeling, and Python
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knowledge of channel modelling and MIMO is a plus. Proficiency with Matlab or Python. Additionally, the applicant should have strong interpersonal skills and the ability to work in an international team. Type
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. Proficiency in data analysis software (e.g., R, MATLAB, SPSS, Primer, Python). Experience with analytical techniques such as ANOVA, PCA, Regression, and Multidimensional Scaling (MDS). Experience in ecosystem
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programming languages such as Python, R, or MATLAB for data analysis and algorithm development. Knowledge of machine learning techniques and statistical modeling for environmental applications. Excellent
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-culture approaches, and skilled in fluorescence microscopy and image analysis for functional read-outs of microphysiological systems Software: Proficiency in Python, MATLAB, or R for data analysis, and
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, programming skills (R, Python, MATLAB) for statistical analysis, and teaching experience in related subjects will also be considered. Languages (1 point): Proficiency in scientific English at level B2 or higher
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students Strong background in laboratory experiment or numerical computation Familiar with Matlab, C++, Python, Java, or other programming language Proficient in one of the software packages: PFC, VISSIM
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processing; experience with array processing or inverse problems is beneficial. Proficiency in programming languages such as C, C++, Python, or Julia/Matlab is advantageous. Background in computational
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-on research experience is a necessity. Proficiency in MATLAB or Python is a significant plus, as is previous coursework/enduring interest in neuroscience, biology, and/or bio-engineering. The ideal candidate