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-funded project TOADAPT, which investigates the social-ecological adaptive capacity of forests across multiple scales and disturbance regimes. Your profile Completed PhD in forest ecology, environmental
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learning or applied mathematics. Required skills and qualities: - Fluency with Python programming for data analysis or machine learning, - Knowledge of statistical or probabilistic modelling techniques
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. The candidate must hold a PhD in Urban or Rural Development, Civil Engineering or related domain. The candidate is expected to have hands-on experience in field related to urban or rural planning, renewable
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management ● Supervising and coordinating undergraduate research assistants ● Helping to mentor masters and doctoral students ● Designing experiments, collecting data, analyzing and writing papers
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. Essential Duties and Responsibilities: Planning experiments, preparing samples, and conducting experiments at LEAF. Data analysis (e.g. fitting transient absorption data, making plots of spectra). Writing
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infection models, multi-omic approaches and data analysis, and/or anaerobic bacterial culture. Learn more: Our benefits , where we prioritize your well-being and success to enhance every aspect of your life
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. Qualifications Applicants with experience in mixed methods, particularly digital methods, data scraping/analysis and qualitative research (Netnography and interviews) will be preferred. Proficiency in English and
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of bioinformatics tools for RNA sequencing data analysis. Previous and experience with RNA sequencing, including sample preparation. Downloading a copy of our Job Description Full details of the role and the skills
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. Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants . This is a term position
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analysis of data including measures of pupil dilation, microsaccades, and behavioral measures of speech perception. Experience with data collection and statistical modeling of time-series data are essential