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-VIS-NIR). Experience in hyperspectral data processing (HMSPL, μXRF, μXAS) and statistical analysis (clustering, machine learning) is preferred. Familiarity with fossilization processes and taphonomic
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quality and consumer satisfaction. You will also apply statistical and machine-learning tools to explore how physical and chemical fiber parameters relate to dye uptake behavior, dyebath exhaustion, color
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as a member of the GHER contributing to the EU research project COMEDI in a consortium of 11 leading partners in the field of data assimilation and deep learning. A successful applicant will develop
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computing, obtained within a maximum of 7 years; application of machine learning and deep learning methods to remote sensing images; proficiency in programming (R, Python, or similar); ability to work
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development of machine learning tools and their applications to medical imaging. Key Responsibilities: The Post Doctoral Associate will apply their technical skills toward the development, implementation, and
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machine learning and data harmonization approaches for water quality monitoring and assessment. Responsibilities include developing and validating computational methods to standardize and infer nutrient and
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statistical software & machine learning (e.g., R, Python, SAS, or STATA). Experience working with large population dataset (e.g. EHR, claims data). Background in health informatics, population health research
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and industrial project partners. You will be responsible for: Developing and adapting machine-learning approaches for structure-based and generative molecular design. Integrating physicochemical
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and/or case-crossover analyses, and have a promising publication record. Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization
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experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and components to automate