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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
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qualitative and quantitative analytical methods to model clinician attention, verbal reasoning, and documentation behaviour Develop and evaluate machine learning models, including unimodal, fusion, and
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to develop generative AI methods for nanoparticle drug delivery design, at the intersection of machine learning, explainability, and pharmaceutical nanotechnology. Job description We are looking for a
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silico approaches. This may include mathematical modeling of biological systems, machine learning and artificial intelligence methods, and the development of innovative algorithms and software pipelines
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related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with transformer architectures (e.g., ViT
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team members. Learn more at: https://hr.duke.edu/benefits/ Equal Opportunity Employer: Duke is an Equal Opportunity Employer committed to providing employment opportunity without regard
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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and distributed control intelligence that can be applied to solve these problems through the application of machine learning, intelligent optimization techniques, automated fault detections and
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Learning Contexts TLTED 5005 - Equity, Diversity, and Justice in Education TLTED 5108 - Teaching and Learning of Mathematics in Grades Pre-K - 5 MATH 1050 - Precollege Mathematics I MATH 1075 - Precollege