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• PhD degree in Science or Engineering • Education or a track record in applied mathematics, with a strong interest in physical sciences • A solid foundation in thermodynamics & statistical
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and collaborative research at the intersection of quantum information science, machine learning, statistical learning theory, quantum thermodynamics, and complexity science. The Research Fellow will
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molecular dynamics simulations to model structural, thermodynamic, and electronic properties. - Contribute to open-source software, benchmarks, and datasets that advance the global materials community
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mechanics, molecular modelling, and/or quantum thermodynamics would also be beneficial. Evidence of quality publications in reputable refereed journals and experience presenting at conferences. Evidence of
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qualification with experience in a relevant area of engineering or technology and very good knowledge of refrigeration technologies and systems, from thermodynamic cycles to components and simulation
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spectrometry Use computational chemistry to understand the molecular structure and thermodynamic pathways that form these newly identified molecules Support simulation chamber studies of I-OOM formation
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synthesis of complex inorganic and materials, structural characterization, and quantitative thermodynamic and thermophysical analysis and process-driven structure–property relationships, metallurgy
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. ROS events modify snow and sea ice geophysical properties, amplifying the thermodynamic coupling at the ocean-sea ice-atmosphere interface. Melt onset timing is an important indicator of the seasonal
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calorimetric measurements (e.g., DSC, drop solution, acid solution, isothermal titration) to quantify thermodynamic and kinetic parameters. Troubleshoot and repair instrumentation and custom-built experimental
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. CALPHAD modelling and thermodynamic assessments. Phase-field modelling of materials behaviour and evolution. The successful candidate will be able to work effectively both independently and collaboratively