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analysis of PFAS. This task notably involves cutting-edge work on data processing. Simultaneously deploy targeted analysis quantification and non-targeted identification of PFAS in samples obtained from
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taxonomy in AI-assisted workflows Prototype and test automated classification scripts (Python/R) Document data pipelines and QA/QC procedures Supervision & Training Mentor PhD-level and undergraduate RAs
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. Qualifications: PhD in machine learning, with experience in applications in computer vision or medical image analysis. Strong publication record in top venues (e.g., CVPR, MIDL, MICCAI, IPMI, PAMI, TMI, MIA
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candidate will have expertise in data analysis/interpretation, publication of results in the scientific literature, and a valid driver’s license. Additional Skills: Strong interpersonal skills, excellent
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training and guidance to junior undergraduate and graduate students. Education: A PhD in Neuroscience, Computational Neuroscience, Machine Learning in image analysis, or a related field, with significant
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data collection and analysis for publication in peer-reviewed journals. The position will also include mentoring of undergraduate students. Qualifications: Research publications, grant writing
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required Experience analyzing wearable device data including accelerometer and GPS data, as well as experience with spatial data analysis Demonstrated publication record in high quality peer-reviewed
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assist working groups and steering committee members in accomplishing project milestones, training PhD and other students, conducting research and analysis, developing dissemination materials, and
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simulations and data analysis, detector and electronics R&D, and experiment-related operations, including: Participating in data collection including shift-taking at PSI Participating in detector and experiment
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& coordination, drafting & revisions of protocols, amendments, & annual reports Data collection Data entry Data analysis and literature review Communications (e.g. develop knowledge mobilization strategy