55 machine-learning-"https:"-"https:"-"https:"-"https:" positions at Nature Careers in Denmark
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imaging, deep proteomics, metabolomics, metaproteomics, and machine learning (ML) approaches to develop diagnostic classifiers, spatial tissue atlases, and identify potential therapeutic targets
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
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will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include Advancing equivariant neural network potentials
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mechanisms and kinetics to stabilize highly active but metastable surface motifs sustainable catalytic processes. Modeling Atomic Processes on Nanoparticles Develop atomistic models and machine-learning
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key agroecosystem variables. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and
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sedimentary archives, to facilitate and use in-situ and remote sensing observations of polar environments, and to acquire skills within VibroSeismic data acquisition, analysis and interpretation. The position
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and teaching environment to its 37,000 students (FTEs) and 8.700 employees and has an annual revenue of EUR 1.106 billion. Learn more at www.international.au.dk/
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(FTEs) and 8.700 employees and has an annual revenue of EUR 1.106 billion. Learn more at www.international.au.dk/
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an annual revenues of EUR 935 million. Learn more atwww.international.au.dk/
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environment to its 37,000 students (FTEs) and 8.700 employees and has an annual revenue of EUR 1.106 billion. Learn more at www.international.au.dk/