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how prophages spread and impact host fitness without interference from background MGEs. All this will be modelled and simulated in silico, and model outcomes will be further validated in the laboratory
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to the above mentioned research area, techniques, skills and other requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants
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disease. We develop supervised, un-supervised, self-supervised and generative models to learn across multiple types of data rather than treating each data modality in isolation. We have a high focus on
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at obtaining quantitative data suitable for mathematical modelling of epigenetic states; modelling is performed in collaboration with Physicists at the Niels Bohr Institute. The BioCenter where we are located
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with colleagues at DTU and IIT Bombay, as well as with academic and industrial partners globally. The main purpose of this PhD position is to develop, implement and assess machine learning models
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R Experience with single-cell RNA-sequencing, in particular analysis of data would be an advantage Experience with mouse models and possession of a FELASA B certificate would be an advantage as both
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components are in use. More specifically, the PhD position will look towards connecting different advanced software tools (of multi-physics and data-based models) simulating the metal AM process
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components are in use. More specifically, the PhD position will look towards connecting different advanced software tools (of multi-physics and data-based models) simulating the metal AM process
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biopsies and advanced, preclinical models. A combination of wet-lab and computational biology, close ties to the clinic, and a wonderful team of early career scientists give us the agility and expertise