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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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Research or a related field Strong knowledge of quantitative and/or computational research methods, ideally in numerical optimization and simulation models. Proficiency in one of the major programming
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@rmit.edu.au Please send your CV to akram.hourani@rmit.edu.au Required Skills: Programming and simulation: strong experience in Python or MATLAB. Mathematical modelling: probability, optimization, or multi-agent
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densification model forced by high resolution output from the regional climate model RACMO to simulate the evolution of the Larsen C firn layer in the recent past (1950 to present) and into the future (up to 2100
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to support condition-based predictive maintenance for gas turbine engines. Cranfield has developed unique physics-based technologies on gas turbine performance simulations, diagnostics, prognostics and lifing
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methods in applied mathematics and computational modeling, this specific project aims to uncover new insights into how blood cells form in both healthy and disease states. A key objective is to model
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AI / ML. The unique inter-disciplinary combination will enable: (i) a-priori biological knowledge infusion for GRN modeling and developing GenAI methods for generating GRNs; (ii) generating simulated
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from satellite, aircraft or ground sensors to understand, model and retrieve parameters relevant to the processes driving our atmosphere, biosphere, hydrosphere and cryosphere. About the research project
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on the hypothesis that the future of building design lies at the intersection of physically sound building simulation models and machine learning (ML) techniques. Key considerations include effectively integrating ML
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for resilient manufacturing systems. This topic will build upon existing theory on modular and reconfigurable manufacturing systems and develop methods and model-based approaches to design and evaluate resilient