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demonstrate good collaborative skills. Applicants must be proficient in both written and oral English. Experience from one or several of the following areas is an advantage: Programming, image processing and
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be proficient in both written and oral English. Experience from one or both of the following areas is an advantage: Modelling and simulations of flow in porous media. Programming, image processing and
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characterisations and advanced characterisations such as X-ray, electron, and neutron scattering and imaging experiments. You will undertake independent and collaborative research and will be expected to write up
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journals. Prototype electrochemical devices based on research results. Undertake additional research responsibilities within the field of expertise. Actively apply for external grants and fellowships with
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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machine learning, statistics, logic, language technology, and ethics. You will be also part of the Digital Signal Processing and Image Analysis (DSB) group in the Department of Informatics at UiO. Francesco
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, language technology, and ethics. You will be also part of the Digital Signal Processing and Image Analysis (DSB) group in the Department of Informatics at UiO. Jarli & Jordan/ UiO via Unsplash Jarli & Jordan
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ethics. You will be also part of the Digital Signal Processing and Image Analysis (DSB) group in the Department of Informatics at UiO. Francesco Saggio/UiO via Unsplash Francesco Saggio/UiO What skills
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. This postdoctoral position is part of the EU cofund research project AMBER, Advanced Multiscale Biological imaging using European Research infrastructures, will address scientific and sectoral gaps in biological