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features in alloys, to aid future alloy design. The UNSW team, lead by A/Prof Patrick Burr, will provide the modelling contributions, and the team at USyd, led by Prof Julie Carney and Dr Ranming Liu, is in
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. Literature review of technologies and commercial market scanning. Modelling, simulation and data management. Design and analysis of hardware and software. Coding, debugging, and troubleshooting. Testing
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computational models to simulate and analyze normative behaviors in various contexts. Data Collection and Analysis: Design and implement empirical studies, including data collection, statistical analysis, and
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electric vehicle (EV) charging infrastructure. The selected candidate will contribute to the modeling, simulation, and prototyping of innovative hardware solutions, including harmonic mitigation and thermal
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, molecular dynamics, and machine learning, to model battery electrolyte and solid electrolyte interphase (SEI), while collaborating with experimentalists. Qualifications • Ph.D. in Computational Materials
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to apply advanced AI models in areas such as catalyst design, multi-scale modeling, and spectroscopic analysis. The Research Fellow will take on a significant role in machine learning theoretical energy
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Transportation Program, aimed at addressing power quality challenges in electric vehicle (EV) charging infrastructure. The selected candidate will contribute to the modeling, simulation, and prototyping
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aviation or aerospace fields and a demonstrated track record of publications in reputable journals or conferences; (c) solid skills in data analysis, modeling or simulation related to aviation systems
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. Proficiency in programming languages such as Python, C++, and MATLAB for spacecraft modeling and simulation. Experience with simulation environments and tools used in space research (e.g., GMAT, STK, Gazebo
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the growth of the company. The need is to develop significantly larger system designs and simulations and to embed the added knowledge into the business as a legacy capability to reduce the reliance