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-engineering-and-automation/nonlinear-systems-and-control ) at Aalto University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees
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on the theory and applications of variational quantum algorithms and quantum machine learning. We also have activity in quantum optics, so additional experience and interest in this topic is considered a definite
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active role in advancing the research projects of the MQS group. Our recent research has focused on the theory and applications of variational quantum algorithms and quantum machine learning. We also have
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Python with demonstrable familiarity with PyTorch, experience in working on shared codebases, excellent applied math skills (especially probability theory, matrix algebra, calculus). Beyond technical
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support the group and complement our research portfolio in the field of optimization. The work comprises combining theory and practice by investigating efficient ways to model and solve demanding industrial
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of optimization. The work comprises combining theory and practice by investigating efficient ways to model and solve demanding industrial problems related to energy and sustainability. The decision levels mainly
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excellent programming skills in Python with demonstrable familiarity with PyTorch, experience in working on shared codebases, excellent applied math skills (especially probability theory, matrix algebra
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studies and interactive AI systems. This position will be funded based on an initial 2-year contract + 2 years extension. The key idea is to apply theories, models, and methods from psychology to improve