34 data-mining-post-doc Postdoctoral positions at University of London in United Kingdom
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/) The post holder will have two main responsibilities. From one side, they will collaborate closely with wet lab scientists in our team and analyse mutational and transcriptomic data coming from experiments
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treatments. To achieve this, we will develop personalised cardiac models at scale, and update these models over time, using imaging and electrical data collected by collaborators at multiple centres. We
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support projects using GWAS, Mendelian Randomisation, and polygenic risk score analysis to uncover genetic mechanisms underlying complex traits. There are opportunities to integrate omics data across
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offer a range of work life balance and family friendly, inclusive employment policies, flexible working arrangements, and campus facilities. The post is full-time, fixed term for 3 years. Salary will be
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develop, synthesise and characterise materials for this project. About You The post is suited to a PhD graduate with a background in materials chemistry or a related discipline. If you have a vivid
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Dr Heli Hietala. The postdoc project involves primarily simulations informed by observations, related data analysis and theory/models, comparing various aspects of shock particle acceleration and meso
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About the Role This post will support two projects: 1) Alzheimer’s Society funded project on Hospital at Home services for people living with dementia. The project is conducting an evaluation
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process for any candidate with a disability. We are open to considering applications from candidates wishing to work flexibly. The post is based in Mile-End. It is a full-time (35 hours), fixed-term (24
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techniques (including PCR) and cell imaging. Previous experience of organ-on-a-chip approaches or in vitro models and experience of working in musculoskeletal tissues is desirable but not essential. The post
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characterise the internal dynamics of neural networks. Topological approach – based on metrics derived from topological data analysis to capture qualitative structural changes in the neural network configuration