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Field
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of their service lives), statistics, emerging data science tools and techniques A demonstrated ability to use a statistical programming language such as R or Python for non-linear data fitting, and optimization
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Qualifications: A PhD in Physics, Astrophysics, Data Science or a closely related discipline is required. Strong proficiency with programming, especially in python and optionally in C/C++. Strict adherence to open
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is required. Strong proficiency with programming, especially in python and optionally in C/C++. Strict adherence to open source software and open science models. Ability and willingness to work as part
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knowledge of IRT models, a basic understanding of common estimation methods, and strong programming skills in R, Python, or another relevant computing language. Experience with machine learning methods is a
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microscopy experiments (e.g. confocal and light sheet) would be advantageous Experience and interest in performing programmatic bioimage analysis (ideally Python) and/or bioinformatics is required Candidate
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preferred: 1. Microsoft Office Applications, particularly MS Word, Excel, Powerpoint 2. Experience in R, Python The successful applicant is expected to possess at least a PhD Degree in Epidemiology, Public
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. Proficiency with data analytic programs (e.g., Stata, SPSS, AMOS, SAS, R/Python, etc.). Experience translating and disseminating research findings to broader audiences (e.g., business leaders, policymakers
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techniques for screening plasmid libraries, such as phage display and yeast display is an advantage. Coding abilities in R/Python are an advantage. The candidate will work in a very ambitious interdisciplinary
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developing and implementing algorithms for processing, imaging or inversion of seismic data, preferably using MATLAB and/or Python. Experience or demonstrated ability in processing and analysis of surface
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is also expected to re-implement historical narrative systems in Python and help determine the course of a larger collaborative CNS project dealing with such systems. About the LEAD AI fellowship