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viability using multiple detection techniques (FACS, microscope, spectrophotometer). Collaboration on the analysis of created bacteria in Zebrafish models. Analyse data, contribute to scientific publications
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. Your job In this PhD position, you will conduct idealised experiments with an atmospheric model (OpenIFS), using concepts from the mathematical field of periodically forced dynamical systems. You will
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underpinnings of chronic pain and the dynamics of host-microbiome interactions in health and disease. Methodologically, we combine molecular biology, biochemistry, pharmacology, preclinical mouse models and the
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comprehensive model of what tranquillity is, the factors that influence it and how to design for it. Attention to design contexts and design processes will be key to ensuring that useful measurements, methods and
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process is poorly recorded and needs improvement. Aims and Objectives In collaboration with the Health Innovation Partnership, a modelling pipeline will be devised to cope with the challenges of data
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systems using various tools and models, including: i) characterization of the emerging patterns in physical systems (solid state materials and active systems); ii) investigation of the mechanical properties
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Trust. The successful candidate will work closely with the PI and a PhD student within a larger cross-disciplinary team to construct a quantitative computational model of carbonate biomineralisation
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computational models for trustworthy applications in critical care. The doctoral position is part of the research initiative “The Digital Society” at ØUC. Project description Intensive Care Units (ICUs) generate
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this, there are further sub-objectives during the investigation to achieve this goal: Predict thermal warpage effects on a supersonic intake at different flight times, coupled to a numerical model for the downstream
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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