13 molecular-modeling-or-molecular-dynamic-simulation Fellowship research jobs at University of London
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extracellular histone, a damage-associated molecular pattern that is implicated in adverse outcomes after injury. This project will include using a range of clinically relevant models as well as in vitro
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. The successful candidate will have a Postgraduate degree, ideally a doctoral degree in a relevant topic in parasitology, zoology, molecular epidemiology, diagnostics or related topic, in-depth knowledge
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with the WASH sector. This includes laboratory experience with safe sample handling, culture, molecular biology (DNA extraction and qPCR) and handling human and environmental samples (stool, water, swabs
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together a group of leading clinical and non-clinical scientists researching the clinical, genetic, cellular and molecular basis of endocrine diseases. Key strengths include close collaboration between
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Infectious Disease Epidemiology & Dynamics department at LSHTM to work on polio eradication. This role utilises global surveillance data for polio to inform understanding of the status of eradication and
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). This global project aims to assess the behavioural and social drivers of vaccine uptake and to identify both supply- and demand-side barriers to immunisation. The post-holder will contribute to modelling
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analysis to join a dynamic team that has, for the past 8 years, developed an extensive body of research on corruption, governance and anti-corruption strategies. In the Accountability in Action project
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to join the Environment & Health Modelling (EHM) Lab (https://www.lshtm.ac.uk/ehm-lab ) led by Prof Antonio Gasparrini. The successful candidate will work on the project CONNECT – Cohort and environmental
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, conducting simulation studies, analysis of datasets from economic and social research studies, software implementation and delivery of workshops. The Research Fellow will be supervised by Prof. Jonathan
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research project on cardiovascular risk prediction for people with immune-mediated inflammatory disease. The successful candidate will use advanced risk prediction methods to develop prediction models