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computational imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation. The project is closely
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imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation. The project is closely connected
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diagnosis of gas turbines. The project focuses on developing an integrated approach that combines machine learning techniques with physics-based models to estimate the health of various system components
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. The project focuses on developing an integrated approach that combines machine learning techniques with physics-based models to estimate the health of various system components. The aim is that fault diagnosis
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, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses
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, Data Science, Machine Learning, or a closely related field. Strong programming skills in Python or MATLAB. Very good communication skills in English, both written and spoken, since the research
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(e.g., COBRApy, COBRA Toolbox, RAVEN). Demonstrated experience with machine learning methods and their application to biological or scientific data. Strong programming skills in Python, with experience
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measurement technique development, atmospheric modelling, and advanced methods for integrating observational and model data through data assimilation and machine learning. About the research project The overall
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your application: A doctoral degree in automatic control, electrical engineering, computational materials science or related. Research experience in battery tests, machine learning, data-driven
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year. You should have knowledge and experience in bridging quantum and classical machine learning, and be fluent in English, both written and spoken. Assesment criteria Qualifications that are considered