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Field
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environments) remains an open frontier. The new MIT Multi-agent AI Postdoctoral Fellowship Program at Schwarzman College of Computing (MIT MAPS) brings together cutting-edge methods in machine learning
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. Researching and developing novel machine learning architectures for integration across multiple types of high-dimensional data. Researching and implementing novel algorithms for analysis of latent factors and
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on understanding how tumor cells evade immune surveillance. The lab is particularly interested in: The regulation of natural killer (NK) cell development, function, and anti-tumor activity. CAR-NK cell therapy
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, Geography, Civil Engineering, Computer Science, Mathematics, Physics or a related quantitative field. Skills and Knowledge: Knowledge of scientific computing, data assimilation, and machine learning
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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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medical, dental and vision coverage effective on your very first day 2:1 Match on retirement savings Responsibilities* Researching and developing novel machine learning architectures for integration across
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assimilation, and machine learning frameworks. Expertise in modeling geophysical systems. Strong proficiency in machine learning libraries such as PyTorch. Proficiency in writing clean, efficient, and well
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genome editing, CRISPRa/i Animal work and rodent surgeries Machine learning / artificial intelligence (using imaging or 'omics data) Responsibilities: Lead independent and collaborative projects; establish
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considered if willing to learn. Interest in tumor metabolism and a strong desire to gain experience in bulk and spatial metabolomics. Solid bioinformatics skills, including familiarity with tools for analyzing
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Data Science, Human Centered AI, and the SLAC Machine Learning Program. KIPAC also has strong ties and active collaborations with theorists at LITP @ Stanford and SLAC. The positions offer a competitive