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expertise. 2. Curriculum Vitae including a list of publications (maximum 3 pages). Where to apply Website https://jobrxiv.org/job/phd-position-in-machine-learning-and-ecology/?utm_sourc… Requirements
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this goal, doped-diamond systems will be considered. The thermal stability of selected compounds under operating conditions will be assessed by means of molecular dynamics simulations with Machine Learning
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, including the programming, AI – tools, machine learning Residence outside the Czech Republic Nice-to-have: Prior experience with microscopy and plant research Experience living or visiting Czech Republic and
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documents uploaded using the dedicated electronic form. helpdesk: petra.koudelova@fsv.cvut.cz Machine learning for 3D printed multifunctional metamaterials Description: The PhD topic focuses on the use
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uploaded using the dedicated electronic form. helpdesk: petra.koudelova@fsv.cvut.cz Physics-guided learning for machine control Description: Robust machine control assumes modeling of robot-environment
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scanning and Time-of-Flight (ToF) sensors, to enable robust material identification directly in non-laboratory, real-world environments. The acquired data will be processed using advanced machine learning
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on tungsten samples and candidate tungsten alloys will validate the simulations and guide the design of more dust-resistant materials. Finally, we will use machine learning to integrate simulation and
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preparation and testing or powder mixtures, and then to devise predictive models (possible using machine learning approaches) for the estimation of mixture properties from pure component propeties. The PhD
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. Approaches researched: molecular dynamics, quantum simulations, machine learning/AI, and high-throughput computing. Required: Mgr./MSc. in chemistry, physics, computer science, or a related field If you are
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Qualifications: Completed doctoral studies – PhD in bio-resource technology, practical implementation of Machine Learning, or a related field. Strong knowledge of Food security theory. Understanding of principles