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- UNIVERSITY OF VIENNA
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, resourceful PhD candidate, with knowledge in aircraft power systems and IT skills in MATLAB/Simulink and other related software. Applicants should have achieved or be expecting to achieve a 1st or a 2:1 in an
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the power of AI/ML and software-defined networking (SDN), and distributed learning methodologies, the research will focus on creating self-configuring, self-optimizing, and self-healing mechanisms for real
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. Experience with molecular dynamics software such as LAMMS is desirable. Experience with molecular simulation software is beneficial. To apply please contact Dr Siperstein - flor.siperstein@manchester.ac.uk
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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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, with expertise in fluid mechanics and heat transfer Experience with OpenFOAM simulation software Programming skills with software such as Matlab and/or Python How to apply Please send an email with
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a relevant field (Architecture, Construction, Environmental Sciences) Knowledge of building physics, retrofit strategies, and energy performance Experience with dynamic thermal modelling software (IES
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Appropriate computational skills and knowledge of programming languages (Python, C++, etc.) Experience with Machine and Deep Learning models and software (Keras, Scikit-Learn, Convolutional Neural Networks, etc
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. Despite some success stories of the use of ultrasound/AE-based technologies for CM of low-speed bearings, high investment cost for hardware and software is the main bottleneck in adopting these technologies
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statistical and/or analytical software packages (e.g., SPSS, R, Tableau, Power BI, etc). How to apply Interested applicants should contact Dr. Stefan Birkett (s.birkett@mmu.ac.uk ) for an informal discussion
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to state-of-the-art laboratories, hardware/software resources, and design facilities, supporting AI-powered electronics research. This project will be conducted within Cranfield’s Integrated Vehicle Health