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Design and implementantion of automatic calibration techniques for fast tune-up Implementation and benchmarking of quantum algorithms Who we are looking for The following requirements are mandatory: A
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existing methods and state-of-the-art in the field. The position includes algorithm design, software implementation, and validation on experimental datasets. You will contribute to building a flexible
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includes implementing and testing machine learning algorithms on quantum control tasks such as state preparation and qubit reset. You will gain hands-on experience with machine learning techniques and their
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and automated floor-plan recognition, to fill data gaps and harmonise information from disparate sources. Learn more and watch our project video here: https://sb.chalmers.se/digital-material-inventories
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, with emphasis on robustness, generalization, and performance in high-dimensional and noisy biological datasets. See this publication for additional details: https://doi.org/10.1111/ede.12449 . The second
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models and algorithms in Python, with documented experience in PyTorch. The applicant should be knowledgeable with neural networks and furthermore have a strong drive towards performing fundamental
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. This involves formulation, implementation, and validation of novel hybrid models. The study emphasizes methodological innovation, scalable algorithms, and translation to industrially relevant multiphase reactors
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multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid approaches for next-generation fluid simulations. Who we
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Are you interested in developing machine learning algorithms that provably help us make better decisions? Join us as a post-doc in the Division of Data Science and AI, Department of Computer Science
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Description of work You will be working in the laboratory of Marta Bally (https://ballylab.com