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Field
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is desired Expertise in the analysis of digital CT scan data is desired Familiarity with analytical tools such as R or Python for statistical and phylogenetic comparative analyses Experience with
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analytical methods for the analysis of linked data from electronic health records and genomic or molecular sources. Strong statistical skills (e.g. proficiency in R or Stata), along with excellent writing
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with chemoreception and sensory biological techniques (SSR, GC-EAD, EAG). •Experience in analytical chemistry (GC-FID, GC-MS). •Experience in or willingness to learn statistical data analyses, data
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Are you passionate about artificial intelligence, data analytics, and cutting-edge engineering applications? Join our interdisciplinary research team and contribute to the future of Non-Destructive
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. This transition is driven by the agri-tech sector, providing affordable and quick assessment tools that empower farmers with soil health data. This project aims to understand farmers' responses to these changes and
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collaborative project that spans multiple continents. You will contribute to the development of new chronobiological analytics on existing data, design experiments to collect novel chronobiological data, engage
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proof-of-concept software tools Machine learning is a plus Strong analytical and programming skills are required (Python, Matlab, and C/C++). Prior proven experience in data-driven innovation projects is
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languages such as Python for data analysis and simulations. Experience with optical systems, astronomical observations, or satellite tracking would be advantageous, although not compulsory. Strong analytical
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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Chemistry, Physics, Materials Science, or a related discipline. The successful applicant will demonstrate strong interest and self-motivation in the subject and the ability to think analytically and