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Field
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: developing and applying image analysis and sensor technologies (e.g. RGB/NIR) for textile production, as well as using machine learning for process optimisation and performance prediction from fibre
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-driven life science framework. Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular
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, especially. multimodal data analysis. Experience on image processing (especially on MRI data) via machine learning. Programming skills (e.g., Python) are required. Ability to communicate effectively in both
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project. The project will also employ a PhD student at Lund University, focusing on developing hybrid architectures for deep learning-based image processing and methods for multimodal medical data. We will
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scenarios. The research combines field experiments, AI-based analysis of museum specimens, and advanced climate modeling to provide process-based insights into the ecological and economic consequences
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are united in our efforts to understand, explain and improve our world and the human condition. Your team SoftiMAX is a soft X-ray beamline focussed on spectro-microscopy and coherent imaging techniques, with
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competence in image analysis and data processing. What you will do Develop and optimize experimental workflows, including sample preparation under different environmental conditions. Design and conduct
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the peculiar optical properties of nanostructures and state of the art instrumentation for optical imaging. The methodology will be explored within the context of drug-target interactions of importance to early
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interactive environment. Facilities include Protein Expertise Platform, X-ray, proteomics, NMR (850-400 MHz), cryo-EM and Biochemical Imaging Centre (confocal, SIM, FLIM, spinning disk, TIRF, STORM). Learn more
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(surgery, medicine, equine sports medicine and rehabilitation). There is also extensive specialist support in related disciplines including anaesthesia, dermatology, ophthalmology, diagnostic imaging