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QUALIFICATIONS: Minimum 2 years of Statistics, Biostatistics, Bioinformatics or related field experience preferred. Experience using machine learning algorithms in Prism, MATLAB, and Python. Skilled in R, SQL, C
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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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, experience with programming in MATLAB, Python, or similar for data analysis is highly valued. Great emphasis is put on personal qualities such as the ability to independently plan and carry out work. You have
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learning methodologies, and tools, with a proven ability to apply these in a research context. Strong programming skills, with proficiency in Python and experience with other research software (e.g., R
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lab/field validation. - Strong DSP/algorithms and real-time control; proficiency in Python/Matlab. - Experience integrating optics with electronics and firmware. - Track record of publications in fiber
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physics, and Brownian motion - Advanced experimental skills in ultra–low-noise measurements - Proficiency in data analysis with Matlab or Python - Practical knowledge in electronics, mechanics, optics
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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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-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