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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and
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, and regenerative constructs. The project combines advanced 2D and 3D bioimaging, including micro/nanoCT, confocal microscopy and SEM, with computational image analysis, computer vision and machine
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knowledge of MR physics Experience with signal processing and/or image processing Experience with Linux systems and High Performance Computing Good oral and written presentation skills in Norwegian
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for the position. Preferred selection criteria Experience with machine learning and neural networks Basic knowledge of MR physics Experience with signal processing and/or image processing Experience with Linux
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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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, microfluidic platform for in‑depth analysis of liver - immune cell interactions Qualifications Master’s degree in Biology, Biotechnology, or a closely related field. Minimum of two years of relevant post
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- immune cell interactions Qualifications Master’s degree in Biology, Biotechnology, or a closely related field. Minimum of two years of relevant post-graduate, hands-on laboratory experience. Documented
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and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured biological data are increasingly common in modern
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
recruitment process, please contact HR Senior Consultant Hedda Winnberg, e-mail: [email protected] . Application deadline: 01.10.2026 ----------------- For practical information about working at NTNU
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. This highly innovative project aims to develop a fully AI driven digital twin that enables real-time optimization and control of fermentation processes. The candidate will develop the digital twin for microbial