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Field
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in healthy states, genetically perturbed states, and during liver regeneration. On the other hand, you will develop algorithms to disentangle direct intercellular signals from those that are induced
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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optimizing these patterns based on the Gerchberg-Saxton algorithm, and then building out an optical setup to test whether these phase patterns can successfully turn a Gaussian beam into the arbitrary predicted
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multiple types of high-dimensional data. Researching and implementing novel algorithms for analysis of latent factors and their dynamics. Conducting literature searches, manuscript preparation, and
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dynamics (algorithms and current practice). Experience in AI/ML. Experience in application performance optimization. Familiarity with parallel programming models (e.g., OpenMP, MPI, CUDA). Experience
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Develop figures, diagnostic algorithms, and graphical abstracts for publications. Prepares visual content for book chapters and peer-reviewed articles, ensuring high quality and clarity. Support for
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for the Metabolic and JMH APRN as needed Annual or Q2 year review of NBS Diagnostic Work-Up algorithms and Emergency Letters with updated management recommendations Performs and documents complete physical
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algorithms for real time neural signal processing. Proficient in a range of machine learning techniques, including neural networks, with particular experience in real-time debugging of algorithms during live
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experience from which comparable knowledge is demonstrated may be considered. Preferred Qualifications Ph.D. degree. Demonstrated expertise in computational biophysics and molecular dynamics (algorithms and
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discipline. At least 3 first author papers in disciplines related to neuroprosthetics. Published first author work in using machine learning algorithms for real time neural signal processing. Proficient in a