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Future. Discover. Together. The Applied Machine Learning (AML) group within the Artificial Intelligence Department is seeking a scientist in the field of Machine Learning to strengthen its research
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Research Scientific Computing Center (NERSC ) at Berkeley Lab seeks a highly motivated Postdoctoral Researcher -- Scientific Machine Learning (NESAP) to join the Workflow Readiness team as part of NERSC's
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background knowledge in statistical genetics, machine learning, omics data analysis, and computational biology. Additional knowledge in transcriptomics, statistics, and foundation models is also desired. Fixed
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in the field of Electrical & Electronic Engineering in the 2021 QS World University Rankings by Subjects. We are looking for a Predoctoral researcher who can work as a Data Scientist, Machine Learning
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and machine learning. We focus on inductive logic programming (ILP), a form of inductive program synthesis which learns logical rules from data. The focus of this position is to develop ILP/program
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part-time positions are available for those committed to application-oriented education. Job Responsibilities Teach and assess at least 3 courses in the following core subjects: Data Analytics & Machine
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expected to contribute to the development and application of advanced manufacturing simulations, and machine learning (ML) models relevant to additive manufacturing, virtual manufacturing, material
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 20 hours ago
image processing, disease detection, diagnosis, and therapeutic monitoring. The program addresses critical regulatory challenges posed by AI devices that can continuously learn and adapt, including
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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) for engineered microbial strains. Design and implement machine learning algorithms to predict strain performance and optimise metabolic pathways. Integrate multi-omics datasets into computational workflows