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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
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thermodynamics and quantum measurement theory - Vojtech Nádaždy – nanomaterials and solar cells - Ondrej Šauša – nuclear physics - Peter Šiffalovič – nanomaterials and batteries - Ladislav Šamaj – statistical
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from date of hire. Preferred Qualifications PVT & Thermodynamic Modeling: Integrate laboratory observations with phase behavior modeling to characterize complex fluid mixtures and multi-phase behavior
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the obtained doctorate must be presented before you can take up the position. Good oral and written presentation skills in English. Strong background in thermodynamics, fluid mechanics, and combustion
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(MOFs). To characterise the adsorption properties of the selected MOFs with water to identify the best performing adsorbent material considering its cost. Then carry out thermodynamic modelling
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understanding of relevant chemistry/physics concepts such as reaction kinetics and thermodynamics, electrostatics and diffusion. Excellent analytical skills and practical knowledge of methods for gas and liquid
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or modelling approaches, including machine-learning applications, thermodynamic modelling, and/or assessment of magmatic copper fertility. Desired capability in field-based geology including detrital sampling
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predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming, mathematics, physics. You will
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corresponding qualifications that could provide a basis for successfully completing a doctorate. A strong knowledge base in applied thermodynamics, and modeling, simulation, and optimization of energy systems