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in designing and building research environments, particularly those supporting data-intensive workflows Working experience at synchrotrons or other large-scale experimental research facilities
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. You have a Bachelors degree or equivalent in physics or engineering, and/or significant experience in the operation of large, complex, scientific facilities. You have strong programming skills, with
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successful in this role you need to have the following qualifications: You have a Bachelors degree in physics or an engineering discipline and/or have experience in the operation of large, complex facilities
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applying for major research grants, particularly at the European level. We also require support in identifying and securing large-scale funding to establish national competence centres and graduate schools
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the operation of large, complex scientific facilities. You are able to communicate fluently and effectively in English, both in speaking and writing. This includes comprehensive note-taking on technical and
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electron laser experimental techniques, - independent handling of large data sets. Demonstrated independence in your work and research ambitions after doctoral studies. Demonstrated development and
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, often related to moisture and durability aspects. The division has a large laboratory infrastructure and has a high focus on experimental work. Subject and project description Biobased building materials
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as scientific visualization, data and information visualization, volume visualization, flow visualization, medical visualization, large-scale image and volume processing, multi-resolution and out
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Description of the workplace The position will be placed at the division of Computer Vision and Machine Learning at the Centre for Mathematical Sciences . The Centre for Mathematical Sciences is an
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Assistant Professor in artificial intelligence (AI) with a focus on precision medicine (PA2025/1776)
creativity and a high degree of expertise in the field, for example in clinical medicine, epidemiology, bioinformatics or mathematical modelling including handling of large-scale data sets. We expect