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of antimicrobial resistance (AMR) genes. By applying high-throughput sequencing and integrative data analysis in clinical and environmental settings across hospitals in Guinea-Bissau, Nigeria, and Côte d'Ivoire
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headed by Prof. Iber, which leverages imaging data to develop data-driven, mechanistic models of biological processes. The team employs cutting-edge computational tools and imaging techniques
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for deployment and test them To excel in this role as a ML-focused robotics engineer, you should possess: Pursuing a master’s degree in data science, computer science, robotics, electrical or software
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exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered. Further information about the IEG group can be found on our
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, Information Technology, Information Systems, Statistics, Data Science, Engineering, or a related field Strong machine learning and programming experience with the ability to work across frontend, backend, and embedded
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evidence-informed policymaking. Please consult www.dec.ethz.ch for an overview of ongoing research projects. Our team: We are an international team of highly dedicated economists and data scientists
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with a high inclination for Earth Sciences. You will help the BedrettoLab team with: Data acquisition with borehole logging techniques and rock core scanning devices Data analyzing with standard software
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are essential to certification processes and outcomes. This project will focus on cooperatives and their workers by expanding existing data from a previous project in Côte d’Ivoire. Cooperatives support farmers
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. The objectives of the advertised PhD position are to: Assume responsibility for recruitment of participants, data entry and data management, development of follow up surveys, testing of psychometric properties
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supervision of Prof. Robin Erbacher, and is expected to play a leading role within the endcap muon CSC and/or GEM detector subsystems, both on upgrades and operations. They will also work on CMS data analysis