9 coding-"https:"-"FEMTO-ST"-"CSIC"-"P"-"UCL"-"https:"-"https:"-"https:"-"https:" positions at Imperial College London
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to energy-efficient artificial intelligence based on temporal neural computation. The project investigates how spiking neural networks, temporal coding, and learned neural delays can enable accurate
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of research governance and a commitment to supporting high‑quality, compliant research activity. The ideal candidate will bring: Strong working knowledge of the Human Tissue Act, HTA Codes of Practice
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publicly available policy documents and datasets Designing and maintaining well-organised datasets that can be queried, analysed, and used in downstream applications Writing clear, reproducible code in R
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maintaining well-organised datasets that can be queried, analysed, and used in downstream applications Writing clear, reproducible code in R or Python to clean, process, and analyse data Supporting
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to support experiment planning, model selection, interpretation of results and coordination of complex experimental and modelling workflows. You will work hands-on with data, code and modelling tools
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to develop a model of health and healthcare provision (see www.tlomodel.org ). The post holder will lead the model analyses that address the three questions outlined above, including, model development, coding
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tools, reproducible code and high-quality publications Presenting findings to Imperial, Oxford and GSK stakeholders and contributing to collaborative project planning Secondments at GSK to test models
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Excellent coding skills Preferably a familiarity with machine learning and deep learning models, as well as a familiarity with cardiology. Ability to work well in a team, and coordinate team research Ability
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molecular and systems-biology models Delivering open-source tools, reproducible code and high-quality publications Presenting findings to Imperial, Oxford and GSK stakeholders and contributing