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robotic platforms, integration of optical and imaging-based feedback, and development of modeling and control strategies for operation in complex biological environments. Particular attention will be given
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Engineering. DCSC is an internationally leading research centre where PhD candidates, postdocs and faculty members work on modeling, analysis, control and optimization of complex dynamical systems relevant
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-) immunoprecipitations from neuronal cell models and brain tissue followed by quantitative LC-MS/MS, and you will run that workflow from end to end – from sample preparation to data analysis and interpretation. Candidate
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in the meninges of murine models of activated PI3Kδ syndrome (APDS), an inborn error of immunity associated with immune and neurodevelopmental abnormalities. In parallel, you will lead the
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neuroscience. Through cutting-edge neuroimaging, computational modelling, and neurotechnology, our network will drive breakthroughs in understanding the human brain and pave the way for personalized diagnosis
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to define glycan features that govern influenza attachment and to develop advanced in vitro receptor models reflecting airway tissues. Your tools: Interdisciplinary receptor analysis: you will work with
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at the Faculty of Social and Behavioural Sciences has a job opening for PhD candidate. In the position of PhD candidate, you are responsible for conducting original research on computational models of semantic
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well as participants with panic disorder; Apply advanced statistical and computational modelling techniques to analyze the SCED data; Collaborate with clinical partners and interdisciplinary researchers; Disseminate
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metrology tools is continuously challenged by stricter requirements and increasingly complex geometries. In this project, you will work on the ideation, modeling, and experimental validation of novel
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modelling for conversion and sorbent regeneration, in conjunction with another PhD student in the department who will perform CFD modelling and other researchers performing process system modelling within