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Field
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. Applications area includes but not limited to multimodal SciML models and deep surrogates for various simulations. This position will be in the Machine Intelligence Group in the Center for Applied
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in vivo perturbation experiments to uncover how genes shape immune-cell states and predict how the immune system responds to interventions. This tight integration of advanced machine learning and
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perturbation experiments to uncover how genes shape immune-cell states and predict how the immune system responds to interventions. This tight integration of advanced machine learning and
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Mathjobs.org | 22 days ago
focuses primarily on geometric analysis, spectral geometry, and differential geometry on singular spaces, preferably with connections to Finsler geometry, discrete geometry, or machine learning. Your
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focuses primarily on geometric analysis, spectral geometry, and differential geometry on singular spaces, preferably with connections to Finsler geometry, discrete geometry, or machine learning. Your
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machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch or TensorFlow) Project funding Other Funding
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experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise : Familiarity with causal machine learning, ensemble methods, and deep learning architectures (CNNs, RNNs). Experience
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, to develop artificial intelligence and machine learning (AI/ML) models that predict therapeutic response, identify clinically actionable patient subgroups, and support personalized treatment
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Posting Number R250178 Posting Link https://www.ubjobs.buffalo.edu/postings/59822 Employer Research Foundation Position Type RF Professional Job Type Full-Time Appointment Term Salary Grade E.89
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collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL ( Prof. Olga Fink ). IMOS develops advanced machine-learning and AI methods for complex engineering and