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System Models, or related simulation tools. Additional knowledge of statistical methods, Monte Carlo techniques, or ensemble approaches is highly valued. The post is advertised as full-time. We are open to
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, an understanding of evolution of foundation models, and a good grounding in multimodal learning, including fusion and alignment. How to Apply Please submit an application via the Queen Mary Portal and upload a copy
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fluorescence-lifetime detection (Fast-FLIM) and temporal focusing. This instrument will deliver quantitative, sub-second imaging of live three-dimensional cell-culture and organoid models, advancing fundamental
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/w) fixed-term postdoctoral position in Simulation and Modelling. The position focuses on designing and implementing the agent-based modeling component of research projects in code, requiring
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solutions informed by the latest ideas in medical imaging AI, computer vision and robotic guidance; and evaluate models in simulated and real clinical scenarios. Evaluation may involve quantitative studies
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project, you will: develop and employ novel advanced biophysical instrumentation based on optical trapping or fluorescence microscopy to study DNA replication; develop and employ simulations and data
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mechanisms of meiotic chromosome segregation using Drosophila oocytes as a model system (http://ohkura.bio.ed.ac.uk). We are looking for an enthusiastic and talented researcher to study spindle regulation in
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, modelling and simulation, CFD, control and experimental investigation would be particularly useful. Candidates close to completion of their PhD will initially be appointed in the junior research training zone
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. The post-holder will have expertise in optimisation modeling, machine learning techniques, excellent programming skills, simulation modeling and a strong track record of developing both academic and publicly
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal