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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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, which has multiple test machines with GPUs and AI accelerators. The algorithms used can be bound by the available compute power or memory bandwidth in different parts of the program. This information will
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. ? Familiarity with text parsing and analysis (natural language processing) and/or machine learning techniques is preferred. ? Candidate must be able to work on multiple projects simultaneously. Applicants should
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University of California, San Francisco | San Francisco, California | United States | about 1 month ago
. Strong background in applied machine learning, biostatistics and/or bioinformatics. Preferred Qualifications Doctoral (PhD) degree in quantitative data science (e.g., data science, epidemiology, health
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researchers and collaborators. In order for research papers and analysis to be conducted efficiently, large quantities of raw data must be well curated. In addition to heading up the data wrangling effort, the
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machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest ideas using historical
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machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest ideas using historical
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, statistical analysis, and machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest
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, statistical analysis, and machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest
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biodiversity or occurrence data (e.g., GBIF). Understanding of species distribution modelling or trait-based ecology. Interest or experience in applying AI or machine learning methods to ecological questions