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wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
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construction, and a robust foundation in statistical spectral analysis, including familiarity with (or strong interest in) chemometrics and/or machine learning algorithms. Job requirements The Ideal Candidate
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of machine learning (ML) and quantum many-body physics. We are also happy to work with experts in one of the two fields who are committed to learning the other. Moreover, we look for interest in developing
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subpopulations, as well as (plastic) cancer cell states that contribute to tumor progression, metastasis and therapy resistance. The candidate will lead several projects applying machine learning to (single-cell
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. You'll be part of the Tactile Machines Lab, which is a dynamic, interdisciplinary group of researcher bringing together expertise in robotics, sensing, machine learning, biomechanics, and haptics
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expertise in robotics, sensing, machine learning, biomechanics, and haptics. The project is funded by a NWO VIDI grant to the PI and provides financial security, access to state-of-the-art equipment, travel
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during your bachelor, master and PhD courses. For more information about this function, please contact dr. K.E. Konrad, email k.e.konrad@utwente.nl, phone: +31 53 489 3906. About the department You will
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for this position, the following is required: PhD in a relevant field such as data science, AI, computer science, machine learning, Earth system science, climate etc. with a thesis subject relevant to the description
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for this position, the following is required: PhD in a relevant field such as computer science, quantum physics, electronic engineering, data science, AI, machine learning, Earth system science, climate etc. with a
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several key academic and industrial partners. You will be part of a diverse and passionate research team of academic staff, PhD candidates and Postdoctoral researchers in the Computer Engineering group