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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 06-Dec-25 Location: New York, New York Categories: Academic/Faculty Internal Number: 178709 A postdoctoral position
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trials to multi-omics explorations, imaging, biomarker development, stable-isotope tracers, and large-scale data set s and machine learning. The lab is now recruiting for a postdoctoral researcher, who
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that preserves object identity or style. They should have a solid publication record in top-tier computer vision conferences such as CVPR, ICCV, or ECCV, and demonstrate proficiency in deep learning frameworks
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allow users to input CDR forcing (e.g., alkalinity addition) and produce day-by-day forecasts of CO2 uptake and storage durability. The project combines physics-based modeling, machine learning, and high
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and expertise in brain imaging (MRI), image processing and machine learning. Coordinating projects within the research group, supervising students and writing applications are also included in the role
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-guided machine learning. You are quick to assimilate information and capable of independent research. Experience in the pharmaceutical sector is an advantage You speak and write English fluently. You are
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 26 days ago
large data set types including RNAseq, DNAseq, RIPseq, and CLIPseq. Experience in gene expression analysis, alternative splicing analysis, machine learning, and motif analysis are preferred. Candidates
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SPECIFICS Postdoctoral Scholar – Public Health Sciences Postdoctoral Scholar of Machine Learning and Statistical Genomics Description: The Department of Public Health Sciences is seeking postdoc scholars
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: Machine Learning / Machine Learning Lattice Field Theory lattice gauge theory Nuclear Theory Nuclear astrophysics Appl Deadline: 2026/02/01 11:59PM (posted 2025/11/04) Position Description: Apply
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: https://ki.se/en/cmb/enric-llorens-group Duties We are seeking a talented and enthusiastic postdoctoral fellow to apply cutting-edge single-cell and spatial genomics approaches in combination with