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Manufacturing and Machine Industry theme of the Aalto House of AI , led by Professor Ville Kyrki. The theme brings together robotics, machine learning, and electrical and mechanical engineering to work on
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: Machine-learning interatomic potentials; High-throughput automated workflows for atomistic simulations; Atomistic modeling of the structure of materials; Simulation of molecular diffusion and
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augmentation tools, and realistic attack scenarios for audio watermarks. Publishing research in leading journals and conferences in speech, audio, and machine learning, and contributing to open-source
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. A strong background in energy-system modelling, mathematical optimization, machine learning, reinforcement learning, or data-driven decision-making is appreciated. Experience with programming and
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quantum algorithms and their optimization, adiabatic quantum computation, and quantum machine learning. Theoretical and applied research into the capabilities of quantum computing and quantum
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computation, and quantum machine learning. Theoretical and applied research into the capabilities of quantum computing and quantum advantage is a prospective topic across four application domains
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optimization, adiabatic quantum computation, and quantum machine learning. Theoretical and applied research into the capabilities of quantum computing and quantum advantage is a key focus across four application
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theory, and expertise with one or several of the following concepts: machine creativity, agency, autonomy, motivation/teleology, value grounding and open-endedness . If in doubt, please contact us with