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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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difficult and the creation of more intelligent process control strategies and innovative methods of tracking reliability can be achieved with expert informed machine learning techniques, which offer more
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. As a Carnegie Doctoral/R2 institution, our world-class scholars instruct about 26,000 students in associate's, bachelor's, master's and doctoral level degree programs. Whether you are seeking the charm
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, emissions, and productivity. Decision-Support & MCDA Implement a machine-learning-driven multi-criteria decision analysis to rank and select optimal decarbonization pathways. Collaborate with industry and
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Free probability theory High-dimensional probability, concentration and functional inequalities Mathematical aspects of machine learning and deep neural networks Free Probability aspects of Quantum
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-Sigler Institute for Integrative Genomics and the Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning
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of natural language processing, machine learning, artificial intelligence, and human-computer interaction. Established within the School of Computer Science, LTI pioneers innovative approaches to understanding
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the Interpretable Machine Learning Lab (https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in collaboration with other researchers on machine learning tools that help humans make better
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robotics, and human-machine interfaces. We are looking for an ambitious researcher who is looking to use this opportunity to grow towards the next level of research. The position is renewable each year based