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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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business datasets (e.g. ORBIS, Foreign Direct investment Data - UNCTAD) and appropriate experience with statistical software (e.g. Stata, R, Python). They will have a good understanding of, and interest in
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our software development team, developing novel scientific algorithms and applications in the areas of spectroscopic analysis and mining of the science data catalogues extracted from the pipelines
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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
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and manage independent research • Ability to set research goals, be self-motivated and proactive • A keen eye for visual presentation, software design, and in writing clear, concise, elegant code
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Experience Experience developing research software using appropriate languages and environements (Python, Julia, Matlab) Knowledge of optimisation problem formulations and solution methods Experience of risk
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driven research Maths competency and experience with statistics in research Software competency: ImageJ, Matlab, Graphpad Prism, R Evidence of Github use An interest in cardiovascular physiology