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Position: Associate Machine Learning Scientist in Geometric Deep Learning
Institution: Genentech, Inc.
Location: New York City, New York, United States
Duties: Participate in cutting-edge research in ML and applications to drug discovery and design; Collaborate closely with cross-functional teams across gRED to solve complex problems including developing models to predict antigen-antibody affinity and developability properties to inform generative model-based design; Refine models and workflows by performing exploratory data analysis, interrogating scientific hypotheses, and rigorous model selection; Contribute to publications and present results at internal and external scientific conferences, workshops, and venues
Requirements: Ph.D. or a Master’s degree with 3+ years of work experience in Computer Science, Statistics, Applied Math, Physics, Chemistry, or related technical field; Experience in implementing equivariant neural networks and molecular dynamics (MD) simulations; Strong publication record and experience contributing to research communities, including conferences like NeurIPS, ICML, ICLR, etc
   
Text: Associate Machine Learning Scientist in Geometric Deep Learning Participate in cutting-edge research in ML and applications to drug discovery and design; Collaborate closely with cross-functional teams across gRED to solve complex problems including developing models to predict antigen-antibody affinity and developability properties to inform generative model-based design; Refine models and workflows by performing exploratory data analysis, interrogating scientific hypotheses, and rigorous model selection; Contribute to publications and present results at internal and external scientific conferences, workshops, and venues Ph.D. or a Master’s degree with 3+ years of work experience in Computer Science, Statistics, Applied Math, Physics, Chemistry, or related technical field; Experience in implementing equivariant neural networks and molecular dynamics (MD) simulations; Strong publication record and experience contributing to research communities, including conferences like NeurIPS, ICML, ICLR, etc
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