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therapeutics. The experience includes opportunities to learn the operation and upkeep of physiological and cell culture instrumentation, apply quality assurance and quality control practices, and contribute
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2). Vascular network engineering and angiogenesis 3). Applications of machine learning in cell and tissue engineering The Disease Biophysics Group is a creative, transdisciplinary group of engineers
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-clinical models and multi-omic approaches. The proposed research involves the application of state of the art methods such as single cell RNA-Seq/ATAC seq/spatial transcriptomics on preclinical animal models
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highly motivated Postdoctoral Research Fellow to join an exciting journey to unravel the principles of how cellular activities drive fundamental brain functions, such as computation, learning, and memory
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sequence-function landscapes, using a combination of empirical data and ML methods (e.g. transformer models). One focus of this work will be on B-cell receptor evolution. Experience in applications of modern
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Qualifications: Ph.D. in Molecular Biology, Genetics, Cell Biology, or a closely related field, with a strong emphasis on cancer research. Demonstrated expertise in molecular biology techniques and omics
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training of excellent researchers through international mobility, advanced training, and interdisciplinary and inter-sectoral collaboration. The programme is designed to enhance the creative and innovative
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learning* Technical qualifications: For bench scientist : expertise in mouse models of cancer, functional in vitro assays, experience with alveolar Type 2 organoid cell culture, experience with tissue
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or mobile/remote work mode are at the discretion of the hiring department. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course
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) Mammalian cell culture (experience with induced pluripotent stem cells and/or adult stem cells preferred) Machine learning and basic bioinformatics A proven solid research experience in the context of multi