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Field
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Position Summary The postdoctoral fellow will develop artificial intelligence applications to support characterization of medical data with a focus on radiology image, radiology reports, and
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long ride, but a ride worth taking! Key Qualifications: Deep knowledge about how biological neurons have been trained before, and new ideas on how to train them Prior knowledge and experience in encoding
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optimization of optical imaging hardware, develop data acquisition software and algorithms for data processing, as well as perform phantom and human clinical studies. This candidate is expected to co-supervise
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration
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typologically diverse languages Creating self-supervised learning algorithms that can assess phonological development and speech complexity in children from birth through age 6, with applications to both typical
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working under the supervision of Prof. Jaideep Vaidya (the PI and Director, I-DSLA) to develop and analyze privacy-preserving solutions for biomedical data research, implementing the developed algorithms
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travel allowance and access to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding
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structure-preserving discretization algorithms (a refinement of finite-element analysis compatible with exact geometric, topological, and physical constraints) with artificial neural networks for achieving
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heavy software development component. The successful candidate will perform research in the application of machine learning (ML) techniques to the finite element method (FEM) in the context of composites
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Number: JR91414 Position Summary The postdoctoral fellow will develop artificial intelligence applications to support characterization of medical data with a focus on radiology image, radiology reports