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immunology Experience with T cell engineering (CAR-T, TCR-T) and/or immunopeptidomics is preferred (but not required). At Dana-Farber Cancer Institute, we work every day to create an innovative, caring, and
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Computer Science, Applied Mathematics/Statistics, Robotics, Physics, or related discipline, with an excellent academic record. Demonstrated expertise in computer vision and machine learning, including object
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/Information Science (e.g., Computer Science, Human-Computer Interaction, AI) or related disciplines, or an equivalent qualification, and must have no more than five years of post-qualification experience at the time of
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in each crop area and learn basic agronomic, data collection, and plant breeding methodologies in trials and nurseries planted at the USDA-ARS. Learning Objectives: The project assignments will provide
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(thedonnellycentre.utoronto.ca ). Required Qualifications: We are looking for postdocs that have excellent molecular biology skills and/or a strong computational background including machine learning approaches. Candidates should
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of genes and proteins as regulators of physiological or immunological traits. Learning Objectives: Under the guidance of the mentor, the candidate will gain experience in and learn to utilize a functional
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related field. Desire to learn new skills and work in teams including students, researchers, and faculty. Excellent computer skills including Microsoft Word, Excel, PowerPoint, and statistical software
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help mentor undergraduate and graduate students and will have the opportunity to teach classes by separate assignment as a part-time lecturer. This position will begin Summer or Fall 2026 according
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Computer Science, Applied Mathematics/Statistics, Robotics, Physics, or related discipline, with an excellent academic record. Demonstrated expertise and leadership in computer vision and machine learning research
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, output validation and reporting. Developing integrative strategies for a diverse set of data, integrating the outcomes to inform future projected trend analysis. Applying statistical and machine learning