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missions operated by LATMOS. The postdoc will employ deep learning approaches using satellite data and ground stations. -Understanding the infrared data from the IASI mission and identifying the channels
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on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and Data Science, Safety and
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data and deep learning methods to assess canopy cover, quality, carbon stocks, and ecosystem services. Mandatory requirements: PhD in areas related to forest resources, remote sensing, data science, or
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processes, the foundations of deep learning. Experience coding with deep learning libraries such as Pytorch/JAX is essential. Fluent written and spoken English skills as well as contributions to the group
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experience Required Qualifications: PhD in Electrical Engineering or a closely related field. Preferred: Demonstrated expertise in AI/ML, including deep learning, computer vision, LLMs, VLMs, multimodal AI
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position We have a vacancy for a PhD candidate in machine learning
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/nanoplastics, and other environmental compounds to assess their potential impacts on human health and the environment using machine learning (ML), deep learning (DL), and big data analytics. His lab is
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UiO/Anders Lien 16th October 2026 Languages English English English PhD Research Fellow in Machine Learning and Statistics Apply for this job See advertisement About the position Integreat
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journal or conference on a relevant topic in machine learning, embedded systems, and edge intelligence Hands-on experience on Nvidia Jetson boards or other edge platforms Strong knowledge in deep learning
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale