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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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required. • Programming skills are required. • Knowledge of Natural Language Processing and Machine Learning is preferred. • Fluent English required, both oral and written. French is appreciated but
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dynamics, data science, and machine learning are beneficial. What we offer: We offer a position with a competitive salary in one of Germany’s most attractive research environments. TUD is one of eleven
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experience in programming and working with large-scale data; expertise in machine learning is a strong plus. Ability to work in a highly collaborative and interdisciplinary environment. Experience implementing
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contribution of genetic and non-genetic driving forces for the cells’ evolution and glioma development. Using multi-omics data integration and machine learning, we will investigate cellular behaviors and gene
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of statistics, bioinformatics, and/or machine learning approaches are desirable but not required. This is a permanent position within the Nature Portfolio. The successful applicant will primarily support Nature
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interdisciplinary, and together we contribute to science and society. Your role We seek a highly motivated bioinformatician or computational biologist who is well versed in the statistical and machine learning
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an excellent scientific track record. Proven expertise in environmental genomics, metagenomics, or large-scale omics data analysis. Experience with machine learning or AI approaches in biological data is an
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Associate Professor of Experimental Physics Focusing on AI-Based Research of Biomolecular Structures
obtained and to solve complex macromolecular structures, the development and use of artificial intelligence and machine learning will increasingly be required. FAU and HZB are jointly appointing a
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utilizes a widely available diffraction-limited spinning disc confocal microscope (although not limited to this modality) for imaging. A single-step, machine-learning based approach is then applied