54 modal-analysis-machine-learning Postdoctoral positions at The Ohio State University
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, behavioral, and mindfulness-based treatments for anxiety; and modeling who benefits most from learning many emotion regulation skills or a single emotion regulation strength. The Post Doctoral Scholar will
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. Particular emphasis to be placed on developing and applying emergent system identification and modal decomposition techniques as well as statistical analysis of large datasets. Candidate will disseminate
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modeling, and/or machine learning models A pattern of well-regarded interpersonal collaborations (i.e., are you someone people enjoy collaborating with?) Additional Information: The Department of Psychology
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goals. The postdoc advises undergraduate and graduate students in the analysis and interpretation of research data. They are responsible for the extraction, analysis and presentation of research data, and
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heterostructures using MBE. Device fabrication using advanced micro- and nano-fabrication techniques. Low-temperature and high-field magneto-electric transport measurements. Data analysis, interpretation, and
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advanced machine learning and deep learning tools to decode the complexity of immune–tumor interactions, integrate multi-omics data at scale, and predict patient responses to therapy. The center works at
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. Demonstrated expertise in interdisciplinary research with technical and clinical teams and patients desired. Experience leveraging machine learning for human musculoskeletal biomechanics applications desired
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. Data analysis, interpretation, and visualization. Preparing scientific reports and peer-reviewed publications. Presenting research findings at national and international conferences. Required Education
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studying deformation mechanisms in refractory alloys as via atomic-scale calculations as well as application of machine learning to materials discovery The ideal candidate will have the following
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data Experience with GIS/RS and database environments (e.g., ArcGIS and Quantum GIS) Experience with machine learning and statistical learning Experience working with large, diverse datasets Familiarity