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cell development and function, and compare the (epi)genetic profiles characteristic of individual genetic defects. These studies will be complemented by modeling disease mechanisms in suitable cell lines
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understanding on the origin of nucleic acids that are shed in liquid biopsies, such as blood, using cancer models (mouse and rat) and patient samples of neuroblastoma disease, a rare childhood cancer. Nucleic
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to determine the molecular structure and function of a TZ sub-complex consisting of 3-5 proteins. You will monitor the gating mechanism of TZ in cellular models such as RPE1 or cultured dopaminergic neurons by
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of predictive models for energy demand and production. These models will leverage techniques such as time series analysis and machine learning and will be integrated into a digital twin platform. The aim is to
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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:100% of the full-time weekly hours Tasks: The PhD student will be responsible for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs
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innovative research utilizing wet-lab techniques, 3D models, and extracellular vesicles to advance our understanding of drug resistance in melanoma. Your responsibilities: Conduct wet-lab research in melanoma
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Senior Researcher in Design and Operation of Sustainable Biomanufacturing Processes - DTU Chemica...
techno-economic evaluation and proven capabilities in the evaluation and modeling of resource recovery and valorization pathways. The role also requires experience in process and supply chain design
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bioinformatic tools to develop innovative models and investigate on lineages commitment, cell type evolution and cell-cell interactions (e.g. Cao et al., Nature 2019; Lemaire et al., Science Advances, 2021; Chen
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. To do this, knowledge or willingness to be trained in advanced statistical modelling, ideally with an interest in methods for causal inference in observational data, is strongly preferred. Using various