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Field
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Monte Carlo methods. Previous experience in ecological data analysis is considered as a strength. We seek candidates with the ability to analyze and interpret complex ecological data and a keen interest
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, qualitative and creative research methods, this research is deepening understanding about the complex psychosocial, cultural and familial processes that influence farm women’s health and well-being across
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conduct high quality research, familiar with tools and techniques for analysing complex systems, qualitative (interviews, surveys) and quantitative data analysis. Technical Proficiency Familiarity with
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curious to deliver work that matters, your journey starts here! Innovation. Interdisciplinary collaboration. Complex problem solving. In Carnegie Mellon University’s Department of Mechanical Engineering
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technologies, including big data analytics, artificial intelligence (AI), remote data transfer, and cloud computing to help solve complex agricultural problems. -The candidate will work on the synthesis
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research, complex data analysis skills, advanced methodological skills, scientific writing, and scientific presentations are ideal Preferred Qualifications: Knowledge of neuroscience Experience with handling
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academia and practice regarding the cost-effectiveness analysis of safety instrumented systems. You will conduct cost-effectiveness analyses by designing cost models to highlight trade-offs in
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skills, ii) be able to develop in vitro protocols and carry out complex novel assays, and iii) have previous experience in human subject research or animal research, including IRB or IACUC protocol
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technologies, computational genomics, functional assays, and integrated data analysis. We are seeking a highly motivated Postdoctoral Researcher who shares our passion for solving foundational problems in human
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is pivotal to support complex decision-making. Through close collaboration with projects stakeholders, you will contribute to the development of data exchange protocols for Digital Product Passports