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technologies (fiber-optic sensors, DIC), and computer science (machine learning tools) in collaboration with de department of Physics. The aim of the BriCE project is to develop a novel bridge monitoring
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Sensing, or related field Experience in atmospheric modeling, satellite remote sensing, or machine learning Programming skills (Python or R) Strong publication record
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, microfluidics, or/and Machine-Learning to join our dynamic research team. The successful candidate will play a pivotal role in advancing our understanding of microbial communities through innovative microfluidic
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machine learning. In addition to leading research initiatives, the postdoc is expected to collaborate closely with graduate students, faculty, and industry and DoD partners. Key responsibilities include
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machine learning. In addition to leading research initiatives, the postdoc is expected to collaborate closely with graduate students, faculty, and industry and DoD partners. Key responsibilities include
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) have strong knowledge of and background in electromagnetic device design, power electronics and machine learning; (c) have hands-on experience in electrical and electronic engineering; (d) have
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(NLP), topic modelling, or machine learning methods; and (e) have peer-reviewed publications aligned with tourism behaviour or mobility research. Applicants are invited to contact Dr Emily Chen
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loop/TAD structures. - Perform comparative analyses versus Populus tremula; apply network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce
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peer-reviewed journals and/or top-tier conferences. Knowledge & Skills: Strong foundation in machine learning, deep learning, and algorithm development. Proficiency in scientific programming (e.g
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the area of efficient and interpretable machine learning systems. The successful candidate will work on projects involving ensemble learning, large-scale data analytics, and high-performance model design