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record in the development of multi-channel digital signal processing in optical communications, and particularly the compensation of non-linear effects from all sources. An ability to learn new techniques
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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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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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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
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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 area of inference, information build-up and learning methods in the general context of the project. Apply established techniques and develop new methods inspired by Bayesian methods and statistical
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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