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to the development and implementation of approaches that interface with living systems through novel materials and algorithms, electric and magnetic fields, ultrasound, optics and targeted radiation, microfluidics
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experience coding in Python and R. Strong understanding of algorithms, data structures, and computational theory. Experience with data analysis and visualization tools to interpret simulation results. Ability
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, Python, Julia, and/or MATLAB Familiarity with machine learning methods and algorithmic modeling for large datasets Experience with LaTeX, Unix/Linux Evidence of outstanding academic achievement Familiarity
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equivalent experience (e.g. graduate school or industry experience). • Substantial experience coding in Python and R. • Strong understanding of algorithms, data structures, and computational theory
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and follow projects through to completion. PREFERRED QUALIFICATIONS Knowledge of STATA, R, Python, Julia, and/or MATLAB Familiarity with machine learning methods and algorithmic modeling for large
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(HPC) best practices, advanced mathematical research into novel algorithms, and the utilization of machine learning techniques for code acceleration and the development of reduced-order "surrogate
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Science and Engineering, or a related area is required. The position will involve developing models and algorithms for the evolution of inorganic aerosols in the atmosphere, building upon the research
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development and application of novel algorithms and machine learning/AI techniques for extracting insights from biological data sets (genomics, proteomics, imaging, neuroscience), and related areas
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econometric analysis and preparing results tables, managing large data sets, handling spatial data, applying machine learning algorithms, conducting computationally intensive statistical analyses, summarizing
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need. Apply AI and machine learning algorithms to software engineering projects in the researcher’s specific domain. Application of Domain Expertise Initiate and maintain open collaboration with