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Number P250272 Posting Link https://www.ubjobs.buffalo.edu/postings/60309 Employer State Bargaining Unit 08 Position Type UUP Professional Professional Appointment Term Term Salary Grade SL4 Posting Detail
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accuracy in link-tracing designs (e.g. Respondent driven sampling) Partial graph data collection strategies for networks (e.g. Aggregated Relational Data) Large scale models for anomaly detection on graphs
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. Network Science. Cambridge University Press. 2016. http://networksciencebook.com/ Wu Z, Trevino AE, Wu E, et al. Graph deep learning for the characterization of tumour microenvironments from spatial protein
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California State University, San Bernardino | San Bernardino, California | United States | 4 days ago
(frequencies, group differences), preparing power point slides/presentations and pivot tables of the data (e.g., graphing, charting, filters), etc. The majority of the work is data management and reporting
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, prepare reports, charts and graphs from standardized formats. Requires the ability to type/keyboard and use a mouse or other pointing device to operate a microcomputer, peripheral and common office
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shuttle and Austin CapMetro buses with staff ID card For more details, please see: https://hr.utexas.edu/prospective/benefits and https://hr.utexas.edu/current/services/my-total-rewards . For more
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computer science using data-driven techniques (graph theory, ICA, machine learning), in other imaging modalities (DTI; MEG), and in multimodal integration will be relevant. Experience with AFNI/SUMA, SPM, FSL
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, containerization (Docker), Kubernetes API development and web-based analytics tools Systems, Optimization, and AI ML/AI for mobility prediction and optimization Graph algorithms, network science Spatiotemporal
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USC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Position Description Advertised Job Summary The faculty of the Department of Mathematics
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computer science using data-driven techniques (graph theory, ICA, machine learning), in other imaging modalities (DTI; MEG), and in multimodal integration will be relevant. Experience with AFNI/SUMA, SPM, FSL