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of MSI advances our understanding of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as
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tasks include development of adequate single-molecule labeling strategies, optimized use of high-precision MINFLUX microscopy, and establishing methods for data precision analysis. What we offer A
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will use machine learning methods to develop affinity ligands. These methods have been transformative for protein design, allowing generation of novel proteins which can suit a precise need. In this 4
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various environmental samples ranging from extinct and ancient animals to consumer food products. These fragments can be detected and analyzed using our specialized wet and dry-lab methods. The objective
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, chemical, biochemical and physical research areas. You will be involved in teaching methodology courses at postgraduate level and provide user training on EM methods. Work assignments include, operation and
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, chemical, biochemical and physical research areas. You will be involved in teaching methodology courses at postgraduate level and provide user training on EM methods. Work assignments include, operation and
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with various disease states. There is also strong research in method and technology development, including the design and synthesis of small organic molecules and biological macromolecules. Development
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, or network-based, Bayesian or matrix factorization methods for multi-omics integration Ability to independently perform data analysis and scientific interpretation based on omics data at an internationally
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). The staff scientist will provide scientific and technical support to research projects related to toxicological method development and mechanistic studies. The role will be integral to maintaining and
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information about us, please visit: the Department of Biochemistry and Biophysics . About the DDLS PhD student program Data-driven life science (DDLS) uses data, computational methods and artificial