463 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" positions at Nature Careers
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collecting relevant data from 2D, 3D or 4D images. Perform computer automated analysis and quality control on large data sets. Liaise effectively with other groups at Janelia to manage multiple image analysis
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biogeochemical modelling and data-driven machine learning approaches at an ecosystem scale to improve our understanding of the fate of nitrogen fertilizers applied to agricultural soils. This understanding will be
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experience supervising student research Technical Proficiency with: Deployment of machine learning and deep learning models Modern JavaScript/TypeScript ecosystems (React, Node.js, React Native) Python web
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development of modern AI and machine learning techniques. The successful candidate will have a shared appointment in both the Department of Civil and Environmental Engineering (CEE) and the Schwarzman College
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network, who spearheads advancements in AI/Machine Learning, Data Science, Environmental Intelligence, Innovations in Health Sciences, Policy Development and Sustainable Societies. THE POSITION Faculty
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, computational biology, systems immunology, machine learning, functional genomics, molecular and single-cell biology, metabolic network and whole-cell modeling, or innovative methods for generating, analyzing, and
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an advantage Extensive knowledge and experience in digital signal processing Know-how of and experience with using machine learning methods for measurement data processing and evaluation is an
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doctorate Carrying out measurements on ceramic composite materials using laboratory X-ray sources and synchrotron radiation sources: spatially resolved X-ray refraction, computer tomography, and X-ray/neutron
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of equivalence with a German qualification. Skills and experience Extensive research experience in the field of artificial intelligence, machine learning, and deep learning with a focus on language
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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and