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Research Center for Molecular Medicine (CeMM), ÖAW | Vienna, Virginia | United States | 2 months ago
on LazySlide ( et al Nature Methods ), our scalable software foundation, and our deep learning framework for age prediction (Abila et al., Nature Medicine, in press) to engineer a body-scale machine learning
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structure modeling in cancer immunotherapy design. Profile A — AI PhD in machine learning, computer science, computational science, or a related field. Strong experience with deep learning (e.g., PyTorch
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hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Reference no.: 6142 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance
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research career. Research and technical training Training will include hands-on work in the following areas: Developing and validating deep-learning and machine-learning models using echocardiography
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University of Massachusetts Chan Medical School | Worcester, Massachusetts | United States | about 2 months ago
Minimum: USD $62,232.00/Yr. Salary Maximum: USD $75,564.00/Yr. GENERAL SUMMARY OF POSITION: Postdoctoral positions in Deep-Learning Omics are available in the Zhou Lab (https://profiles.umassmed.edu/display
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developing new ones, including machine and deep learning methods. There will be opportunities for development of new cell line and animal models and testing, evaluation, and analysis of new genomic
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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highly desirable. Proficiency in programming languages such as Python or C++. Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch) and advanced data analysis tools. Excellent problem
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learning and deep learning. Specific Requirements PhD in acoustics. Knowledge of acoustical measurement techniques, as well as physical acoustics. Some experience in acoustic signal processing and machine
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. You are expected to bring deep mechanistic insight, drive the scientific narrative, and leverage our robust pre-clinical modelsâ”complemented by our clinical trial multi-omic dataâ”to build your own