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Management and Strategy Posting Number R260147 Posting Link https://www.ubjobs.buffalo.edu/postings/64432 Employer Research Foundation Position Type RF Professional Job Type Full-Time Appointment Term Salary
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, pumps, valves operated by a computer and the corresponding software Develop flow cells to connect various spectroscopic tools to the setup Create and validate reproducible automated workflows
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 12 days ago
department in 1964. We believe that computer science’s greatest purpose is in working with other scientists to solve problems in their own disciplines. Therefore, we actively seek opportunities to generate new
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 5 days ago
department in 1964. We believe that computer science’s greatest purpose is in working with other scientists to solve problems in their own disciplines. Therefore, we actively seek opportunities to generate new
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, Python, R, or comparable platforms). Solid background in signal processing, statistics, time-series analysis, network science, or computational neuroscience. Excellent written, verbal and computer skills
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Knowledge, Skills, and Abilities Ability to read, write, speak, understand, and communicate in English sufficiently to perform job duties. Must be computer literate with proficiency and working knowledge
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statistical techniques in clinical research, database management, and machine learning/artificial intelligence (AI) would be an advantage. The appointee will work with the Principal Investigator on various
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is to detect cognitive strategies in single-trial EEG data. In very broad terms, we will use the HMP method (https://github.com/GWeindel/hmp ) to discover cognitive operations in existing data, label
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Area (EEA) in accordance with the GDPR (the provider's declaration in a continuously updated version can be found at https://cloud.google.com/terms/data-processing-addendum/ (Appendix 3: Specific
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their added value against simpler machine-learning baselines; train and evaluate ARCA on large-scale microbiome datasets, with attention to sparsity, batch effects, scalability, generalisation across studies