2,037 data-"https:"-"https:"-"https:"-"https:"-"University-at-Buffalo" Postdoctoral positions
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, innovative research and team-based health care delivery. What we want The Postdoctoral Research Associate, under general supervision, conducts lab experiments, analyzes and communicates data in the form
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it is imperative that samples are promptly received and processed in a timely and rigorous manner, and all data and methodology is recorded in an organised and precise manner electronically
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Posting Details Position Information Posting Number PG191610PD Position Number 00111577 Position Type Post Doc FLSA Departmental Information Department ID 350101 - Office for Faculty Excellence
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Posting Details Position Information Posting Number PG191550PD Position Number 00111607 Position Type Post Doc FLSA Departmental Information Department ID 150401 - Forestry & Environmental Resources
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of computational biology, Genomics, machine learning, and data science, contributing to the development and evaluation of advanced algorithms for analyzing large-scale biological datasets. This role is ideal
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to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services
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diagnostic pathways. Core research tasks include planning, conducting and publishing epidemiological studies using large-scale observational data, primarily register-based, with a focus on effects of ADHD
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), supernovae, gravitational wave sources, magnetic activity in exoplanets, and machine learning classification. The candidate will lead and participate in observations and data analysis of these sources in all
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coordinating the research collaboration between the Africa Business School and the Growth Lab at Harvard Kennedy School. The primary focus of this position will involve data collection and analysis using
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on “ Integrating AI into Aquatic Ecosystem Models to Decode Ecological Complexity ” funded by Villum Fonden. Within that project, the focus is on exploring novel ways to infer information from environmental data