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Description The Department of Earth and Spaces Sciences at the University of Washington seeks a Postdoctoral Scholar to work on numerical simulations and data analysis to inform the search for life
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the operation, software, and computing of the ATLAS experiment. Required Knowledge, Skills, and Abilities: Ph.D. in experimental particle or nuclear physics or related field. Experience with data analysis and
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, troubleshooting and routine upkeep Develop, optimize and validate sample-preparation and data-analysis workflows for spatial metabolomics, lipidomics and multimodal studies Act as contact for internal and external
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Max Planck Institute of Biophysics, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | 21 days ago
(imaging) data analysis is preferred. Prior experience in microscopy is desired but not required.) Development of 3D diffusion-based single-molecule sensors (the candidate with theoretical knowledge and
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characterise the internal dynamics of neural networks. Topological approach – based on metrics derived from topological data analysis to capture qualitative structural changes in the neural network configuration
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that all animal protocol rules and regulations are followed. Compile and analyze data with appropriate statistical analysis, conclusions, and future directions in support of future CHI research. Keep up
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Spaces Sciences at the University of Washington seeks a Postdoctoral Scholar to work on numerical simulations and data analysis to inform the search for life on exoplanets. The position will be supervised
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-oncology, focusing on the role of myeloid-derived suppressor cells in solid cancers. The successful candidate will have experience in isolation and analysis of myeloid cell functions, strong hands-on skills
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Knowledge of qualitative, participatory, and creative research methods Experience in analysing qualitative data using thematic analysis and analysis software (e.g., NVivo) Demonstrated ability to work
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from