36 data-"https:"-"https:"-"https:"-"https:"-"UNIV" PhD scholarships at Nature Careers
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funded by a major research project from the Novo Nordisk Foundation (NNF). The successful applicants would become part of the Data Science & Statistics section at the department. The successful candidate
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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for working within an interdisciplinary research team Strong analytical skills and ability to work independently Ability to convert experimental data into useful functioning models Preferred qualifications but
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The Centre for Machine Learning located under the Data Science and Statistics Section of the Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark invites
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foundation of existing data to characterise the degradation of aggregated proteins or organelles. The PhD candidate working on this project will utilize state-of-the-art methodology and combine in vitro
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diseases. You will contribute to the acquisition of multi-omics data from cryopreserved patient-derived samples, working in close collaboration with clinicians to assess patient outcomes. You will present
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highlight the potential of microbiome intervention in immuno-oncology. In our team, we actively integrate translational data, functional assays and preclinical models, to understand and overcome mechanisms
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basic cell biology, e.g. CRC1450, TRR332 and SFB1348. RESPONSIBILITIES: Experimental investigations including cell biological, immunological, biochemical and microscopy methods Documentation of data and
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collective behaviour Statistical and computational physics for complex data Astrophysical and space plasmas Space environment, space weather and technological impacts Magnetic confinement fusion energy Sea ice
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biodiversity and functioning (Deadwood2soils) Nitrogen fixation in mycorrhiza and forest soils Linking forest genetics and with operational harvesting data Forest biodiversity – methods for inventory and