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related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with deep learning, computer vision, medical image analysis
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deep learning, computer vision, medical image analysis or unsupervised learning is an advantage. English language skills, both written and spoken Qualification requirements PhD stipends are allocated
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universities. The transition towards electrified and energy-efficient energy systems poses significant challenges in predicting the coupled behaviour of thermofluid, electromagnetic, and rotordynamic processes
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We hereby invite applications for one or more PhD position focusing on the development and application of hyperspectral imaging technologies for bulk forensic evidence analysis. The project aims
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other relevant stakeholders. The PhD student will contribute to the prototype development, feasibility test and evaluation of innovative cross-sectoral pathways and will be involved in data collection
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Help build the systems that will electrify industry Industrial energy systems are about to change fundamentally. For more than a century, industrial processes have been designed as isolated
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PhD Scholarship in University Students’ Learning Via Digital Twins of Neutron Scattering Instruments
. The digital twins will allow students to perform virtual experiments. ACCESS will make digital twins for ESS instruments on imaging (ODIN), inelastic scattering (BIFROST), small-angle scattering (LOKI) and
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to molecular hydration. The student will develop experimental methodologies and combine these with state-of-the-art characterization techniques, including Magnetic Resonance Imaging (MRI), Nuclear Magnetic
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of novel methods to decompose the heterogeneity of psychiatric disorders, particularly ADHD, using large-scale genetic, imaging, and epidemiological data from Denmark and other countries. You will apply
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imaging, NGS, etc.) shared between the research groups at BRIC (www.bric.ku.dk) and the neighbouring Finsen Laboratory. We have weekly journal clubs, data clubs, seminars with invited speakers and a young