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. The candidate should be willing to learn basic physical and numerical modeling methods. The successful candidate will demonstrate commitment to the timely completion of deliverables and strong potential to work
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-efficient, open-loop optimisation of fermentation control profiles, building on recent theoretical developments in optimal control theory, reinforcement learning and numerical methods as well as laboratory
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-chemical properties similar to conventional kerosene, their combustion behavior can differ significantly, requiring adjustments and optimization of current gas turbines (GT). In this context, numerical
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credits* in chemical engineering, mechanical engineering, applied mathematics, or a closely related field. Strong background in computational modeling and numerical methods Experience with multiphase flow
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computational engineering, mathematics, computer science, physics, engineering or a related field Strong background in numerical methods and machine learning Proficiency in at least one programming language
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prior familiarity with the basics of quantum algorithms. Knowledge of numerical methods for solving differential equations is an asset but not a requirement. Women and underrepresented minorities
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interaction, advanced numerical modelling, experimental techniques, sensor development, data-driven methods, and artificial intelligence for industrial applications in energy, process, and materials engineering
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: MatLab and Python. Editing scientific texts: LATEX. Numerical calculation software: ANSYS or similar. Specific Requirements Knowledge: Numerical methods applied in engineering. onstruction and
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training covering topics such as computational modelling, numerical methods, statistical analysis, machine learning or data-driven analysis of complex systems Experience 0–3 years of postdoctoral experience
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system SCHISM. Key words: numerical modelling, numerical methods, nature-based solutions, saltmarsh, coral reefs. Employer and hosting lab description In a higher education and research landscape that has