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pathogenesis and their potential as therapeutic agents and diagnostic tools. Using in vitro, ex vivo, and in vivo models, the lab studies EVs derived from amniotic fluid stem cells (AFSCs) and other perinatal
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include applications of neural networks to the analysis of multi-omic data, models for predicting phenotypes using genotype data, biological data integration, etc. Participation in these projects will
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the collection of empirical data through field trials and the development of prediction models based on these data. The candidate is to perform a variety of functions related to research. The candidate is expected
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the collection of empirical data through field trials and the development of prediction models based on these data. The candidate is to perform a variety of functions related to research. The candidate is expected
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for machine learning in materials science, you will work in close collaboration with members of the WASP-WISE pilot project on predicting moisture content in timber drying using machine learning
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from vascular lesions and blood, combined with genetic, clinical/epidemiological and imaging parameters from patients. We also perform in depth functional studies in animal and cell culture models
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, Chemistry or related scientific fields and experience and knowledge managing and analyzing spectroscopic data to build predictive models. The Successful candidates should be able to work independently, have
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relying on up-to-date research, the program strives to help growers produce high quality vegetables while minimizing pesticide inputs. The program also develops real-time GIS-based predictive models of pest
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analytics, predictive modeling, and related fields. We welcome applicants committed to addressing complex challenges in sport analytics, tourism analytics, human performance, and related fields. Candidates
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 2 months ago
the form of graphs to analyze and predict food-effector systems. Key Responsibilities Develop Probabilistic Machine Learning Models to integrate graphs and food-related omics data Multi-omics integration