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will develop an internationally visible research program in AI-assisted drug discovery at the interface of chemoinformatics, molecular modeling, machine learning, and experimental pharmaceutical research
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técnicas de machine learning e IA. Sólidos conocimientos en análisis y tratamiento de datos, programación y desarrollo de modelos analíticos. Experiencia con bases de datos y herramientas de análisis
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. The University of Michigan offers an exceptional environment for computational and data-driven pharmaceutical sciences. Faculty benefit from world-class resources in artificial intelligence, machine learning, high
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Assistant/Associate Professor of Practice in Human-AI Interaction, Machine Learning, and Data Design
developing, training, and deploying advanced AI, data, and machine learning systems from a human-centered innovation lens. Preference will be given to candidates whose background includes i) experience
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for entry into a PhD program. A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable. You are curious about combining physical modeling
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
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modelling (e.g., LSTMs) and deep generative/unsupervised anomaly detection techniques (e.g., VAEs). *Strong programming proficiency in Python and familiarity with standard data science and machine learning
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qualifications: Experience with molecular modeling and/or structure-based drug design Salary: Commensurate with experience How to apply: Interested applicants apply online at https://slu.wd5.myworkdayjobs.com
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for latent variables and their connections to modern machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The successful candidates
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scientific-programming skills, practical experience of computational model development, and an interest in applying machine-learning or data-driven methods to physical systems. Experience of QTFs, BEM software