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Field
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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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evaluate machine learning approaches for predicting clinically successful drug targets. For this work, the postdoc will have access to a large high-performance compute cluster and to AbbVie's cutting-edge
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the very people who bring those lessons to our students. Every bit of knowledge, and every advancement, provides our students with an unprecedented, collaborative learning environment, and helps improve
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findings into manuscripts for peer-reviewed journals. Learning Objectives: Throughout the course of this research project, the participant will have the opportunity to explore the latest trends and research
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AI to predict safety outcomes for multiple targets and combination therapies Collaborate with research teams and data scientists to design data-driven strategies using machine learning/AI methods
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strong, demonstrated interest to conduct academic research in a relevant field Interest in legal research Interest in causal inference and social science Experience with machine learning / deep learning
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transmitted to the larger organization. Qualifications § A PhD in Electrical and Computer Engineering, Applied Physics, or a related field § >3 years experience with Lumerical and Comsol electromagnetic
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balance of supervised investigation and work experience in a learning environment that will expose the participant to activities across the drug development process. We are seeking scientists from U.S
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clinically validate innovative predictive models utilizing AI and machine learning; test cadaveric anatomy study implementation Complete Data collection and finalize evaluation of AI predictive models
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apply cutting-edge machine learning algorithms, with focus on foundation models and LLMs/agents, to analyze complex biological data. This data includes gsingle cell genomics profiles, spatial data, and