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discipline. Desirable Experience in machine learning, deep learning, data analysis, numerical modelling, or scientific programming (such as Python, MATLAB, or R) is desirable. Knowledge of hydrodynamic
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machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The successful candidates will have the opportunity to collaborate closely
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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design
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. Familiarity with automotive systems, electric vehicles, and embedded BMS constraints. Experience with system identification, uncertainty-aware modelling, large datasets, and machine learning. Evidence of
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deep-learning and 3D computer-vision models that detect features while representing a distribution of plausible interpretations. Encode geological relationships in a knowledge graph that stores
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demonstrable experience with programming in Python and implementing statistical or machine learning algorithms. You have experience with software development practices such as testing and version control with
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of mill and production operations. The scientific challenge will be to use the model and machine learning alongside live mill data (temperature, rolling loads etc) to reverse engineer the current
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metabolomics, lipidomics, proteomics and genomics, and combine these data using statistical and machine-learning approaches. Established markers such as neurofilament light chain (NfL) and GFAP will provide a
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advanced machine learning techniques Optical design and miniaturization of a novel spectroscopic sensor based on freeform optics, optimized for agrifood applications For this function, our Brussels
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Position Description The project will focus on the development and application of advanced data-analysis techniques for gravitational-wave science, including machine learning and deep learning