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animals and humans, contacts with the environment are not avoided and sometimes even actively sought. We will deploy this inspiration from biology to design truly robust machines with distributed control
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experience-driven lifelong learning. Our world-renowned experiential approach empowers our students, faculty, alumni, and partners to create impact far beyond the confines of discipline, degree, and campus
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and machine learning. Topics of interest in this area include, but are not limited to: natural language processing, large language models, graph learning, prompt engineering, knowledge graphs, knowledge
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Do you want to advance inverse modeling for continuous‑ and discrete‑time systems? We invite applications for a postdoctoral position in the group of Axel Ringh at the Division of Applied
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to evaluate pancreatic cancer pathology using human tissue specimens Assemble analysis pipelines using machine learning to process tissue data reproducibly and at scale Conduct analyses using programming
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: https://ki.se/en/cmb/enric-llorens-group Duties We are seeking a talented and enthusiastic postdoctoral fellow to apply cutting-edge single-cell and spatial genomics approaches in combination with
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
accessed using the link above. Jiawei Zhou Ph.D. The candidate will be co-mentored by Dr. Yanguang (Carter) Cao and Dr. Jiawei Zhou to advance the development of artificial intelligence/machine learning (AI
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours ago
intelligence/machine learning (AI/ML) and pharmacokinetic/pharmacodynamic (PK/PD) models that reduce reliance on animal testing and enable the quantitative evaluation of safety and efficacy for antibody and mRNA
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competitive ERC. The project focuses on the development of a first-principles, machine-learning-accelerated computational framework for modelling polymorphism, anharmonicity, and electron–phonon interactions in
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, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving