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the Finnish Center of Excellence in Quantum Materials . Your role and goals The research will focus on developing and using machine learning algorithms to discover novel materials and to build generative models
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-on experience building or modifying equipment and working effectively within multidisciplinary teams. Experience with Linux, ROS/ROS2, electronics and sensor integration, algorithm design, and laboratory
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inference in engineered systems, including telecom networks; The development of neuromorphic algorithms and spiking neural models with built-in efficiency and reliability guarantees; The design of reliability
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manufacturer. Based on our publication (ACS Appl Nano Matter 2022, 10.1021/acsanm.2c03406) and our ongoing collaborative work, we have developed a new chemical assay coupled with a machine learning algorithm to
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characterization and benchmarking techniques for quantum algorithms on devices at or beyond the 16-transmon scale. They will also contribute to designing large-scale control and readout multiplexing based on cQED
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an excellent publication record. Solid research experience in one or more of the following topics is expected: Graph neural networks Optimization algorithms Predicting structured output Self-supervised learning
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digital twins using prediction-powered inference to enhance reliability assessment; The theoretical analysis and algorithmic development of methods rooted in statistical learning theory, multiple hypothesis
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research in neuro-symbolic AI, with a focus on using generative and agentic AI, as well as AI standards to create trustworthy information resources. This includes the design of algorithms, tools, and process
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lasers with high-average and high-peak power Building of optical cavities Running existing simulation codes in Julia and processing their results. Helping to develop new models and algorithms to simulate
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agentic AI, as well as AI standards to create trustworthy information resources. This includes the design of algorithms, tools, and process for knowledge workers to better organise knowledge bases and