74 parallel-processing-"International-PhD-Programme-(IPP)-Mainz" positions at SciLifeLab
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researcher who wants to contribute with research that can provide breakthroughs in how tomorrow’s therapeutic antibodies should be designed to cure previously difficult-to-treat cancer patients. You are
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sinks, recent research has shown that they can also emit methane, nitrous oxide, and underexplored VOCs. Microbial activity is key to these emissions, but we need to understand how different processes
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various diseases, increased knowledge is needed about the mechanisms that control the maturation of inflammatory cells in the bone marrow, the inflammatory processes in tissues, the associated blood
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. Candidates are further expected to have experience in processing and analyzing high-throughput genomic sequencing data and in statistical analysis. Previous experience with Drosophila melanogaster or other
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, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab
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, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab
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and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS
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complementary cytosine-rich sequences. These intricate structures are thought to function as critical protein-binding sites, influencing essential processes such as transcription, replication, telomere stability
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biological basis, through technological processes and mathematical support. Your profile We are looking for a candidate with proficiency and documented research experience particularly in immunology and/or
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variability in risk factor susceptibility, treatment response, disease pathogenesis, and clinical diagnosis (biostatistics, machine/deep learning), ii) Investigating causal processes and disease mechanisms