724 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" research jobs in United States
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Computer/Information Sciences Internal Number: A-179059-11 General Description The Johns Hopkins University Data Science and AI (DSAI) Institute welcomes applications for its Postdoctoral Fellowship program
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aspects of research, conducting field research and development of physics guided machine learning models. The primary project focuses for this Part Time Research position deepening the understanding
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, quantitative polymerase chain reactions, next-generation sequencing, and related, advanced molecular biology techniques. The person will instruct staff and students, participate in planning experiments, and
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techniques; large data processing and high performance computing; advanced causal inference and statistics; computer vision and novel applications of machine learning. Advanced knowledge of R or Python is
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. Preferred Requirements Experience with archaeological survey and excavation, lab work, GIS/GPS/computer graphics, spreadsheets and word processing, and detail-oriented. Essential Functions Duties
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 2 months ago
Reponsibilities Perform analysis of large-scale quantitative and qualitative datasets as part of research projects. Use of new tools (machine learning, LLMs, OCR) to collect and process data. Work with bank and
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Qualifications ? Ph.D. in Physics, Materials Science, or a related field with a concentration in electron microscopy methods ? Experience in the collection and processing of TEM/STEM data ? Computer programming
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large sample libraries; performing in-depth analysis of proteins, peptides, and small molecules in beef and pork products that vary in consumer quality; and applying machine learning techniques to predict
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, age, marital status, disability, public assistance status, veteran status, sexual orientation, gender identity, or gender expression. To learn more about diversity at the U: http://diversity.umn.edu
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morphology (e.g., geometric morphometrics, machine learning), and phylogenetic comparative approaches. We have: • An engaging, supportive, and collaborative research environment. • Opportunities