167 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" Fellowship positions
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Experience in Multiphysics modeling in solid mechanics framework Experience in non-linear solid material response and fracture modeling Experience in machine-learning modeling for solid mechanics applications
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Experience in Multiphysics modeling in solid mechanics framework Experience in non-linear solid material response and fracture modeling Experience in machine-learning modeling for solid mechanics applications
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–functional modeling of root system architecture. Phenomics data integration and high-dimensional trait analysis. Predictive breeding and quantitative genetic modeling. Machine learning approaches to
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Informatics, in the Scientific Computing and Machine Learning (SCML) research group. Starting date as soon as possible/by agreement. The fellowship period is three (3) years. Depending on the
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, machine learning, data science, drug discovery, and related fields are encouraged to apply. Strong computational backgrounds (AI/ML foundation models), proficiency in at least one programming language
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, especially the areas of partial differential equations, integral equations, fast algorithms, spectral and high-order methods * machine learning, especially the areas of optimization, learning theory
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fellowships in machine learning await. Collaborate, innovate, and thrive! PhD Fellowships in Knowledge-Driven Machine Learning in Norway (8 positions) Apply for this job See advertisement About the positions
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Applying machine learning frameworks to satellite datasets Cloud computing Modes of Work Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department
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(e.g., NDVI, EVI, land surface temperature) with environmental datasets Experience with geospatial analysis, GIS, and large environmental datasets Experience developing predictive or machine learning
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Qualifications* - PhD in Mechanical Engineering, Robotics, Computer Science, Electrical Engineering, or a related field - Strong background in computer vision and/or machine learning, with hands-on