240 parallel-computing-numerical-methods-"Multiple" Postdoctoral positions at Nature Careers
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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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, single-cell transcriptomics, proteomics, metabolomics, and network reconstruction. Former trainees of the Chi lab have been principal investigators in multiple academic institutions. Please also see the
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platforms. In addition to mechanistic studies, the lab also specializes in the discovery and development of drugs for the treatment of diabetes and obesity. Our research program is well funded by National
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particular focus on digital methods and tools. The C²DH's ambition is to venture off the beaten track and find new ways of doing, teaching and presenting contemporary history of Luxembourg and the history
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across molecular biology, ecology, bioinformatics, and environmental science. The taxonomic scope is broad and inclusive: we aim to collect comprehensive data across multiple taxonomic groups to support a
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Are you interested in neuromorphic spintronic and can you contribute to the development of the project? Then the Department of Electrical and Computer Engineering invites you to apply for a one year
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research efforts. PREFERRED QUALIFICATIONS: A strong background in murine work, high throughput methods, RNA biology, and/or computational biology are highly desirable. Excellent verbal and written
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studies using both qualitative and quantitative methods to investigate youth development and social inequalities. The Centre maintains close collaborations with national and international research
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strong expertise in data analysis and computational methods for high-dimensional biological datasets, as well as proficiency in R and/or Python programming. Previous experience with single-cell and/or
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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