30 phd-mathematical-modelling-population-modelling Postdoctoral positions at University of Cambridge
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modelling the coupling of atmospheric and micro-physics moisture dynamics. The work will be carried out in collaboration with and under the supervision of Professor Edriss S. Titi. Duties include mathematical
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and Innovation Associate to join this ambitious project. You should hold a PhD in a relevant field such as applied/pure mathematics or physics and have an established track record of original research
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A position exists for a Post Doctoral Research Associate in the Department of Applied Mathematics and Theoretical Physics, funded by the High Energy Physics group's STFC Consolidated grant
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University of Cambridge, Department of Pure Mathematics and Mathematical Statistics Position ID: CambUK -RESEARCHASSOCIATE [#26302] Position Title: Position Type: Postdoctoral Position Location
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the cellular and molecular mechanisms driving tumour development. In this role, you will support a team of scientists using genetically engineered mouse models (GEMMs) and transplantable tumour models (e.g
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, BRCA2, and PALB2. Through advanced single cell genomics, in vivo modelling, and immune profiling, the team will study early molecular and cellular changes that occur in high-risk breast tissue. The team
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, to contribute to cutting-edge research on the early detection and prevention of primary intestinal tumours using animal models of ageing. The project involves a range of advanced techniques, such as complex mouse
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complex behaviours, including learning, in the small invertebrate model organism C. elegans. We have recently discovered a range of novel dopamine receptors in C. elegans and found that different receptors
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data models for electronic health records of people with mental disorders, under the guidance of Dr Osimo and Prof Murray at Cambridge, and a line manager to be selected at Akrivia Health. The aim
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work on large-scale understanding of coastal wetlands - primarily mangrove forests and tidal marshes. This will include mapping and modelling of distribution, value, condition, and opportunities