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solving You are highly experienced in a research environment with a well-established publication track record You have formal training in mathematical, statistical, and machine-learning-based analysis
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positive and supportive team environment. You prefer to stay organized and take pride in completing your tasks in a thoughtful and structured way. As a formal qualification, you must hold a PhD degree (or
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to monitoring key performance indicators (KPIs) of the reManuFactory project. Engage companies through Co-Labs, Pilots, and Drop-In formats, to be demonstrators of the methods developed in the wider reManuFactory
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University of British Columbia | Northern British Columbia Fort Nelson, British Columbia | Canada | about 11 hours ago
analysis. · Expertise in nonlinear multi-species models, bioeconomic models, or related quantitative methods for natural resource management. · Proficiency in programming languages such as R, Python
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live in. Your role Research related to the following areas: Mathematical statistics, Machine Learning, High-dimensional statistics, Robust estimation methods, Probabilistic foundations of mathematical
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contribute to teaching. Qualifications: You should have (or be close to achieving) a PhD degree. Background within computational methods for inverse problems, ideally tomography. Experience with development
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performance computing numerical methods in our state-of-the-art open source micromagnetic model, MagTense. MagTense is based on a core implemented in the Fortran programming language, and it relies
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: Extensive experience in machine learning methods, tools, and platforms. Proficiency in Python, with demonstrated software development experience. Hands-on experience in MLOps, including the design and
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, antibodies, and their complexes using techniques such as cryo-electron microscopy (cryo-EM), X-ray crystallography, and supporting biophysical methods. Responsibilities will include protein expression and
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and