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limited. To learn from past warmer climates and better understand the link between climate and extremes, we can use proxy-based climate reconstructions and climate models for past warmer climates. However
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, or probabilistic modeling, and be proficient in Python and modern machine-learning frameworks (ideally PyTorch). Experience with single-cell transcriptomics, epigenomics, proteomics, spatial omics, or multimodal
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of results. Highly motivated and have good communication, project management and organisational skills. Willing to learn new skills and techniques. Desirable Experience in proteomics and cancer models would be
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-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically
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University of California, San Francisco | San Francisco, California | United States | about 12 hours ago
, including the ability to abstract information requirements from real-world processes to understand information flows in computer systems. Ability to represent relevant information in abstract models. Critical
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with course integrations Maintaining functionality of the DF virtual reality equipment Developing 3D models to provide solutions for the DF unit needs Data entry related to DF projects and processes
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English and Chinese Able to work independently and in a multidisciplinary team Experience in image analysis packages such as Freesurfer, FSL, SPM, or 3DSlicer, or using machine learning or artificial
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spending accounts and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Position Description Advertised Job Summary
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will have the opportunity to learn and apply advanced data analysis techniques, including machine learning and econometric modeling. Through collaboration and technical expertise, this position supports
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/Master’s degree in statistics, mathematics, computer sciences or a related field Thorough knowledge of methods in event history analysis and multi-state models is required. The candidate should be familiar