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. Candidates with strong track records in computational tropical geometry, non-archimedean analysis and geometry, machine learning, and related areas are encouraged to apply. Hybrid and flex working arrangements
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of a catalogue of material composition of satellites 2. Developing material degradation models and estimating the material properties based on life and degradation. 3. Developing machine learning
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of advanced energy storage solutions and integration into the smart grid ecosystem. Implementation of machine learning techniques for predictive maintenance and fault detection in power systems. Exploration
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the simulated ribbons, likely involving machine learning techniques, which will then later be applied to satellite observations. The successful applicant will also be encouraged to interact with other members
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computing, cloud computing, machine learning, artificial intelligence, programmable networks, digital twins, and cyber physical systems. Skills Demonstrable ability to work cooperatively as part of a team
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closely with the computer systems technical managers. They will also have significant interaction with all members of CfAI and IPPP including PhD students, Postdoctoral Research Assistants, Technical and