10 phd-in-simulation-engineer Postdoctoral positions at Aston University in United Kingdom
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techniques from statistical physics, Bayesian inference, and complex systems theory to address challenges posed by noisy and incomplete data. You will contribute to method development, simulation and
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the University consistent with personal needs and aspirations and with the strategic goals of the Institute. To support the development of further research proposals. To assist in the supervision of PhD students
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conference presentations. You should hold (or be near completion of) a PhD in Chemical Engineering, Environmental Engineering, Materials Science, or a related field. Essential skills include hands
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. The ideal candidate must have or be close to having a PhD in a relevant academic discipline. They will have experience in at least one of the research project areas, be able to collect data from multiple open
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candidate will have completed a PhD or a PhD near completion in cell biology, bioengineering, bioscience, molecular biology or a related field. Candidates should have extensive experience of working with
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Aston Institute for Membrane Excellence (AIME) has been awarded £6.1m in funding from the UK Government Department of Science, Industry and Technology (DSIT), with a further £7.1m in potential co
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Aston Institute for Membrane Excellence (AIME) has been awarded £6.1m in funding from the UK Government Department of Science, Industry and Technology (DSIT), with a further £7.1m in potential co
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quickly is highly desirable and additional training will be given as needed. Candidates with post-doctoral experience or who have recently submitted a relevant PhD and are awaiting award will be considered
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Aston Institute for Membrane Excellence (AIME) has been awarded £6.1m in funding from the UK Government Department of Science, Industry and Technology (DSIT), with a further £7.1m in potential co
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closely with data scientists to interpret and predict MFA data using nonlinear reaction-diffusion models, 13C-isotopomer analysis, and MATLAB-based simulations enhanced by Bayesian Machine Learning