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necessary. Applicants who are interested in DA research and development should have a strong background in using DA systems (such as GSI or JEDI) and a strong understanding of the data sets and algorithms
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range of exciting projects: for example, developing machine-learning approaches for the identification of known and unknown metabolites in MS/MS data; meta-analysis of mass spectrometry-based metabolomics
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analysis, and algorithms of computational research questions at a level sufficient to converse with Princeton’s world-class researchers - With the team, regularly meet with, listen to, and ask questions
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, statistics, data analysis, and algorithms of computational research questions. This may involve independent research, studying existing code bases, and keeping up-to-date with publications. Build awareness
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limited to the development and implementation of approaches that interface with living systems through novel materials and algorithms, electric and magnetic fields, ultrasound, optics and targeted radiation
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to the development and implementation of approaches that interface with living systems through novel materials and algorithms, electric and magnetic fields, ultrasound, optics and targeted radiation, microfluidics
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quantum science and engineering. Candidates in quantum theory are encouraged to apply, including quantum information theory, quantum algorithms, quantum error correction, quantum systems theory, and
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background in using DA systems (such as GSI or JEDI) and a strong understanding of the data sets and algorithms used in DA systems. Experience with machine learning is also welcome. Selected applicants will be