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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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spatio-temporal regularization, discrete tomography, low-dimensional latent representations and machine learning. The ultimate aim is to reduce the carbon footprint for the construction industry and enable
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, you must hold a PhD degree (or equivalent). The successful candidate must moreover exhibit the following professional and personal qualifications: Strong background within machine learning learning, and
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. theses at the interface between structural engineering and machine learning. You will disseminate your research through peer-review publications and participation in international conferences. You will
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activities within the areas of embedded software. The position requires a PhD degree within a relevant area (e.g. software, computer, or control engineering) and the desired candidate is expected to have
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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project. Your profile We are looking for a highly motivated candidate with a background in machine/deep learning, and communication networks. The required qualifications include: PhD in computer engineering
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background in modelling, optimization, and control approaches in energy systems, Research vision and potential Familiar with machine learning techniques Background in programming tools such as Python and Julia
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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