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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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, biomedicine, and other areas of societal importance. Coding and/or machine learning experiences are highly valued. Specific projects may involve developing multiscale simulation methods for quantum mechanical
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learning methods development and application. The postdoc associates will be exposed to rich multi-omics data, a variety of diseases, advanced statistical and machine learning methods and wide collaborations
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of Vienna). About the position: Lead the research group focusing on hybrid quantum algorithms, quantum neuromorphic computing, and quantum machine learning Build your own team (PhD students, postdocs) 4-year
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to significantly extend our existing team’s capabilities for data scoring and analysis (e.g., with expertise in natural language processing, machine learning, or computational modeling). Finally, the
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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Surgery Stanford Departments and Centers: Surgery, General Surgery Postdoc Appointment Term: 1 year Appointment Start Date: July 1, 2026 Group or Departmental Website: https://med.stanford.edu/gensurg
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and Liu, Supervised learning in physical networks: From machine learning to learning machines, PRX 11, 021045 (2021) [2] Stern and Murugan, Learning without neurons in physical systems, Ann Rev Cond
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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of belonging. The Postdoctoral Associate also will participate in weekly seminars with other Active Learning Initiative postdocs, receiving training and support in designing and implementing research-based