-
. 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
-
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
-
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
-
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
-
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