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the start of the position Have practical experience in machine learning with Python, including training neural networks in PyTorch or a similar framework Have a solid background in signals and systems as
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on continual reinforcement learning for real-world systems. The grant provides a stable, well-resourced four-year research environment to tackle a problem that classical RL theory is not built for: real systems
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learning. Theoretical and applied research into the capabilities of quantum computing and quantum advantage is a key focus across four application domains: bioinformatics, plant breeding, biomaterial
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approximately four years. You can find more information about Aalto’s doctoral program and its requirements here: https://www.aalto.fi/en/programmes/aalto-doctoral-programme-in-chemical-engineering What we offer
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emphasis on developing the payload for the ESTCube-LuNa mission. Building on previous demonstrations such as ESTCube-1, ESTCube-2, and Foresail-1p, the project aims to apply lessons learned to create a more
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. The research will be carried out at the Department of Information and Communications Engineering, DICE, at Aalto University, Finland. The project environment offers excellent infrastructure for deep learning
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for deep learning, speech, and audio research, including Aalto University’s large-scale scientific computing cluster with CPU and GPU nodes, access to CSC’s national computing infrastructure including LUMI
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, curious and ambitious team of researchers. Aalto offers a vast array of professional development opportunities to grow and learn based on your interests and needs. We offer a fixed term contract initially
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. The applicants should have demonstrated the ability to teach competently and to publish internationally. The position requires fluent skill in English. Finnish and other language skills are considered a merit
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design projects and industrial partners into courses) can teach industrial manufacturing processes for scaled production as well as some one-off prototyping skills is able to apply digital fabrication