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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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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 7 hours ago
of New Hampshire invites applications for a non-tenure-track Assistant Research Professor position in Sustainable Engineering. We seek applicants with a strong background in systems analysis and simulation
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experience in regression analyses (SEM, multi-level, discrete choice behavior modeling…) based on statistical software (R, STATA or SAS) ;•Excellent skills in GIS, Python is an asset ;•Excellent communication
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, and state-of-the-art infrastructure. Our model empowers visionary leaders to accelerate drug development and harness AI-driven innovation to address unmet medical needs. Benefits of Working with Great
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Your Job: In this position, you will be an active part of our Simulation and Data Lab for Applied Machine Learning. Within national and European projects, you will drive the development of cutting
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dynamics and tissue morphogenesis during embryo development using cellular, molecular and mechanical approaches. Cell movements underlie tissue patterns and shapes. Using chick embryos as the model system
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-reviewed journals Hands-on experience with both cell culture systems and mouse models This is a full-time, on-campus position based in the Andrus Gerontology Center. Some travel may be required
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validating deep learning models for the prediction of disease progression from ophthalmic data. Skills include working with image or computer vision-based toolkits, development of multimodal, multidata type
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the area of electric power grids. Essential Duties and Responsibilities: • Perform cyber-physical system modeling and simulation for the power grid. • Perform research on power system dynamics and
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Mandarin); and the ability to work independently as well as in collaboration with a multidisciplinary team. Proficiency in longitudinal analyses, and state-of-the-art statistical and epidemiological models