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Field
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qualification is required at time of hire. Required Qualifications Completed Ph.D. in Ecosystem or Landscape Ecology (or related field such as ecosystem modeling, biogeochemistry, or climate change ecology
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such as the NEPS. Potential research areas include (but are not limited to): Item response modeling of achievement tests Analysis of process data (e.g., response times) to enhance competence measurements
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• Strong quantitative and programming skills; experience with seismic data analysis or numerical modelling is highly desirable• Excellent written and verbal communication skills• Ability to work
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with a background in cognitive psychology, data science or computer science and a willingness to develop skills in computational models of cognitive processes, statistical methods, and programming (R
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capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives, including large-scale
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perfusion as people age with various comorbidities (hypertension, hypotension, obesity) will preserve oxygen supply to the brain. MRI and two photon imaging are used in animal models to understand
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inferential statistics, model testing, and power analysis Desired Qualifications Demonstrated mentoring and teaching ability in the areas of statistical methods and use research-related tools Experience with
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impact, leveraging one of the highest-quality financial datasets in the industry. What You’ll Do Conduct research and develop ML models to enhance trading strategies, with a focus on deep learning and
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contribute to ongoing cancer risk modelling projects within the Centre for Cancer Genetic Epidemiology (CCGE), based in the Department of Public Health and Primary Care. They should have a strong understanding
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are linked to research on composite hydrogen tanks, composite propellers for drones and finite element modelling of textile manufacturing. All research will be conducted with leading companies in