276 data-"https:" "https:" "https:" "https:" "https:" "University of Kent" Postdoctoral research jobs at Nature Careers
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(available for download on the following site ). Please complement with your CV, publication list, and recommendation letters. If confidential data exists that would strengthen the proposal, please indicate
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or device prototyping Experience with quantitative measurements and data analysis Ability to work independently, keep clean documentation, and deliver to milestones Background in thermoelectrics, sensors
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engineering, analytical chemistry, and computational methods for analyzing sequence information and molecular structures is required to perform research tasks A background in food science or food technology is
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research sections with around 350 highly skilled employees, of which approximately 50% are scientific staff. More information can be found here . We believe in encouraging inclusion, acceptance, and
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/ Multi-Omics Data Integration Candidates with a strong background in bioinformatics, computational biology or epigenomics. Desirable experience includes single-cell and spatial omics analysis (scRNA-seq
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neurodegeneration both within as well as across -omics modalities, leveraging both in house as well as public databases. Expertise in the data analysis of one of the modalities that we will use in this project
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to the faculty’s departments. Consequently, your employment will as of that date be with a department. Contact information For further information, please contact: Professor Troels Skrydstrup, +45 28 99 21 32, ts
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of Gothenburg in Sweden and the Norwegian Institute of Public Health (Oslo). We investigate genetic and environmental factors affecting human gestational age at pre-term birth. We work with unique genomic data
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candidates should submit an application package containing 1) CV, 2) contact information for three research references, and 3) a brief summary discussing your research interest or fit with the lab research
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will