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for general image classification tasks. We are particularly interested in applications involving weather radar imagery and medical imaging. Mandatory requirements: Ph.D. in Computer Science, Mathematics
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 17 hours ago
throughput cell-based assays, flow cytometry, confocal microscopy, UV-vis, dynamic light scattering (DLS), immunofluorescence imaging, HPLC, TEM, ELISA, Gel electrophoresis, western blot digital micromirror
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account and navigate to the Career icon on your Dashboard. Click on USYD Find Jobs and apply. For a confidential discussion about the role, or if you require reasonable adjustment or any documents in
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Professor or Research Associate Professor to join the nuclear medicine and molecular imaging instrumentation laboratory in the Molecular Imaging and Theranostics (MI&T) group. The MI&T group has a long
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/PhD in Geosciences, Environmental sciences, remote sensing or computational sciences or image vision and related fields Language skills: Excellent English skills, German is beneficial Desirable skills
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requirements and priorities. Strong skills in data analysis, image processing, and programming (e.g., Python/C++). Demonstrated experience in publishing scientific journal papers. Fluent in English (oral and
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. For more information see: https://medicine.iu.edu/faculty-labs/witczak Successful candidates will have recently completed PhD training, possess a strong background in type 2 diabetes, metabolism, and/or
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Candidates with experience in flow cytometry, single-cell analysis, quantitative imaging, and routine use of R or Python for data analysis and visualization will be especially competitive. The ideal candidate
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and economy that respect people and their environment. We are looking for our next postdoctoral researcher in computer graphics and machine learning to join the Image, Data and Signal (IDS) department
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. For more information see: https://medicine.iu.edu/faculty-labs/witczak Successful candidates will have recently completed PhD training, possess a strong background in type 2 diabetes, metabolism, and/or