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neurodegeneration both within as well as across -omics modalities, leveraging both in house as well as public databases. Expertise in the data analysis of one of the modalities that we will use in this project
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Your profile: Strong background in molecular and cellular biology techniques Experience with experimental design, data analysis, and scientific writing We offer you: EGYM well pass, corporate benefits
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the program will receive training in genomic analysis, experimental modeling, translational science, and preclinical modeling of childhood hematological malignancies. The training program will equip the next
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measurements A good understanding of advanced physiological techniques Experience with enzymatic in vitro assays and plant x climate interactions Experience in complex data handling and statistical analysis
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intelligence (AI)-assisted image analysis for bioinformatics and medicine. The project is highly interdisciplinary, involving areas of microfluidics, fluidic mechanics, biomedical imaging, and machine learning
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fellow, you will be empowered to own a project end-to-end, from model design and assay development to data generation, analysis, and publication. You will be immersed in a scientifically rich and
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Postdoctoral position in Bioinformatics/Computational Biology (m/f/d) (full-time position 100 % ~ 38
; Guthrie J et al., Sci Adv 2023, Haladik et al., Cell Reports Medicine 2025). The position will focus on the computational analysis of multi-omics datasets (genomics, transctiptomics, proteomics
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to explore new analysis methods using deep learning. Depending on the interest, it is also possible to be involved in the wet lab. Professor Tiwari also leads the Center for Integrated Multiomics in Precision
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collaboration in research networks Your profile PhD in Bioinformatics, Computational Biology, Systems Biology, or a related discipline Experience in multi-omics data analysis and handling big data Experience with
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-read whole-genome sequencing data. Genome of Europe will operate in a federated analysis paradigm, with raw-datasets being held separately in different member states, hence the central opportunity