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). The emergence of data-driven techniques (broadly grouped under the term “machine learning”) challenges the traditional foundations of controls and represents an alternative paradigm that cannot be ignored
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applications* in close collaboration with other discipline experts (software, microelectronics and applications engineers). * except for RF payloads. ** including artificial intelligence and machine learning
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English, both spoken and written. Additional experience that would be considered a plus: - some experience in programming and the use of computer algebra in algebraic geometry; - experience with
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spoken and written. Additional experience that would be considered a plus: some experience in programming and the use of computer algebra in algebraic geometry; experience with noncommutative algebraic
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; experience with AI and machine learning methods, especially in the areas of natural language processing or graph neural networks; the ability to work independently and collaboratively in an interdisciplinary
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PhD* in a topic such as animal acoustics, machine learning, quantitative ecology, quantitative biology, signal processing or similar. Evidence of the ability to conduct high-quality research and write
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, mythical scenes of making, or depictions of making in sacred or military contexts). They will take into account (where applicable) how images interact with epigraphic frames and contexts of production and
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of making in ancient Roman visual culture (e.g. depictions of craftsmen at work, mythical scenes of making, or depictions of making in sacred or military contexts). They will take into account (where