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conduct high-quality and impactful research. A key focus of the position is the study of the role of machine learning and artificial intelligence in healthcare and public policy, including
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are open: Optimization under decision-dependent uncertainty (contact Giovanni Pantuso, gp@math.ku.dk ) Monte Carlo methods for high-dimensional statistics and machine learning (contact Jun Yang jy@math.ku.dk
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enzymatic assays, protein work (metabolites) and western blotting. Finally, skills in statistics and machine learning are favored. Documented experience in international research environments and documented
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crystal handling, SPR machines, and two libraries of fragments (small molecules <300 Da) tailored for SPR and X-ray crystallography. This setup enables a full workflow for fragment-based drug discovery
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. Experience with machine learning techniques for neural data analysis. Track record of publications in high-impact journals and successful grant applications. Work Environment: This position is part of a
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, statistics). Excellent organizational skills and attention to detail in experimental design and data tracking. Working knowledge of machine learning techniques for high-throughput data interpretation
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include data science management and development of novel and executing existing computational methods including machine learning and deep learning methods to integrate genomics, transcriptomics and
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history analysis and possibly also with machine learning. Another specific example is statistical software development, e.g. implementing tools and algorithms for working with causal discovery
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programmers. You will also work closely together with course teams to develop data generation, data analysis, modeling, simulation, and machine learning workflows as well as develop custom data science-related
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piglets who are unable to acquire own mothers’ colostrum. The majority of the experimental in vivo work is located at Frederiksberg Campus, albeit but with some investigations under commercial farming