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knowledge about recent neural network architectures for machine learning (e.g., CNNs, RNNs, GANs) have considerable experience with a deep learning framework are curious about the cross-field between signal
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, and deep learning. A Ph.D. in Statistics, Mathematics, CS/EE (with a focus on statistics/machine learning) or a directly related field at the time of appointment is required. The successful applicant
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bioinformatics methods have made significant strides, AI approaches - particularly deep learning - are revealing patterns and relationships in biological data that were previously inaccessible. As a postdoctoral
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with deep learning libraries (e.g., PyTorch) Ability to organise and prioritise work to meet deadlines with minimal supervision Strong written and verbal communication skills, with the ability to convey
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, operations, and culture of DOE. As a result, fellows will gain deep insight into the federal government's role in the creation and implementation of energy technology policies; apply their scientific, policy
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Control engineering (experience with nonlinear systems is a plus) Machine learning and deep learning in context of physical systems Programming skills are required, with Python experience preferred. A good
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/C++; hands-on experience with deep learning libraries (e.g., PyTorch) 5. Ability to organise and prioritise work to meet deadlines with minimal supervision 6. Strong written and verbal
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Preferred Qualifications: Experience with one or more of the following: Building novel 3D and super-resolution ultrasound systems. Developing deep learning algorithms for 3D biological data. Designing and
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on histopathology image data. As a postdoctoral researcher you will be involved with development and validation of AI/deep learning solutions for precision medicine and patient stratification (patient outcome
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in research and development of sustainable energy conversion technologies. We are recognized as global leaders in this field, supported by state-of-the-art facilities and deep expertise. Our