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supplied by Infineum Ltd. • To incorporate Machine Learning (ML) algorithms into the calculation of the forces on the constituent particles, so as to significantly speed up the algorithm. • To incorporate
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collaboration with good oral and written communication skills. Previous research experience in machine learning, deep learning and/or computer vision is essential.
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of vehicle emissions' impact on air quality using data-driven methods and machine learning. The information gained will be used to determine the required mix of vehicles (i.e. petrol, diesel, hybrid, electric
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main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
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in computer vision and intelligent transportation. Experience with tools such as MATLAB, Python or machine learning frameworks is highly desirable. Supervisor: Dr Ning Zhao (N.Zhao@bham.ac.uk
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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
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Master’s degree in a relevant discipline (cognitive neuroscience, neuroscience, computational neuroscience, psychology, cognitive science, machine learning/data science/AI). Start date: 1 October 2025
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modelling and simulation techniques and software packages would be an advantage. Programming skills in languages such as Python, C++, MATLAB, are desirable, as is an awareness of machine learning or other AI
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to microelectronics and space hardware. The aim is a machine learning approach that can build a model from experimental and operational data, but with sufficient physical insight to ensure that the model is robust and
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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning