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Field
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training algorithms and AI architecture. Image reconstruction, segmentation, and classification. High performance computing for spatiotemporal data. Major Duties/Responsibilities: Develop foundation AI
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in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several
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system with integrated sensors. You should hold or be near completion of a PhD/DPhil with relevant experience in the field of robotics, biomedical engineering, information engineering, electrical
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About Us We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with experience in: Deep learning Medical imaging computing
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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signals and initiate downstream responses, with a particular emphasis on the mechanistic basis of activation and small molecule modulation. We investigate pathways involving cytosolic DNA sensors
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/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and
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Iterative Algorithms: Optimization and Control.” About the Project The focus of the project is the analysis of iterative algorithms arising from time discretizations of nonlinear evolutions of various kinds
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magnetometry measurement and analysis of the Nab spectrometer magnetic fields, help develop the BL3 DAQ and algorithms, carry out Monte Carlo simulations for Nab and BL3, and help undergraduates at EKU finish