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, single-cell analysis, and machine/deep learning (preferred but not required). Strong programming and statistical skills (e.g., Python, Perl, R, Bash). Track record of first-author research papers. Strong
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, computational, and machine learning/AI methods, with a particular emphasis on deep learning approaches improve our understanding and prediction of infectious disease dynamics. Projects are also strongly grounded
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platform that utilizes deep learning to analyze images of bruises. Responsibilities: Responsible for developing components of the project platform or deep learning application; Supervises a team of graduate
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Fellow in EEE to contribute towards NLP-based research and associated deep learning applications. The Research Fellow/Research Associate is also expected to support teaching activities as required by
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discipline. Experience and deep understanding of X-ray scattering experiments Experience with development of analysis code for experimental data in Python or similar language Strong interest in learning new
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from date of hire. Preferred Qualifications Aptitude and experience with: (a) predictive machine and deep learning techniques, (b) statistical analysis, (c) hands-on experience using models such as
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other equipment purchased. 7) Mentor undergraduate and graduate students with their projects and teach how to use new instrumentation. 8) Contribute ideas for new research projects. 9) Stay informed
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receive hands-on experience that provides an understanding of the mission, operations, and culture of the DOE. As a result, fellows will gain deep insight into the federal government's role in the creation
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medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and astrostatistics. These posts
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building new collaborations between the University and relevant partners • Supporting internal analytics to help determine focus/strength areas of commercializable research • Developing a new deep technology