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interdisciplinary team at the Technische Universität Dresden, focusing on the integration of multi-omics data to better understand, diagnose, and treat metabolic diseases (i.e. obesity, diabetes, metabolic
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and disease risk factors Investigate new approaches in the analysis of large neurophysiological datasets We are seeking a highly motivated candidate with: A strong master’s degree in psychology
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extraction, sequencing library preparation, Hi-C, ATAC-seq etc.) Experience with bioinformatic analysis of large sequencing data sets Strong statistical skills using R Desirable: Experience with assembly
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, diet, and immune system to investigate their roles in various diseases, including but not limited to cancer, metabolic disorders, and infections. The research design involves the integration of large
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cutting-edge big/deep data analysis methods, including machine learning and artificial intelligence. The ideal candidate will therefore have a strong background in data science and in the application and
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of the extracts; LC-MS and bioactivity-guided isolation and structure elucidation of the purified metabolites Supervising and training students Maintain analytical equipment Cooperate with other large collaborative
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Area of research: Scientific / postdoctoral posts Job description: Postdoc for "Large-Eddy Simulations of Arctic air-mass transformations" (m/f/d) Background The Arctic climate is shaped by
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the development of computational methods for genetics, high-throughput omics data and causal discovery. Our interdisciplinary and international team is jointly located at DKFZ and EMBL Heidelberg, and
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or related with strong bioinformatics background) (m/f/d) with focus on the analysis of cutting-edge liquid biopsy proteomics, transcriptomics and other omics data sets related to blinding diseases, such as
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PostDoc in "Sustaining the keystone: Rethinking Antarctic krill fishery management under climate ...
ecosystems. Your Profile A PhD in marine biology, conservation biology, fishery management & conservation, or related fields A strong background in handling large data sets, programming (preferably in R