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single synthetic program of computational geometry. Specific interests include morphology, design topology, discrete differential geometry, packings, and machine learning methods for unstructured geometric
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. Additional Qualifications Attention to detail and professionalism are highly important for the role. Experience with participant recruitment and retention methods. CITI certified and trained in Good Clinical
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of Massachusetts would be a plus but not required. Attention to detail and professionalism are highly important for the role. Experience with participant recruitment and retention methods. CITI certified and trained
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improve their financial outcomes. The projects would be developed and completed in collaboration with CBA. Primary methods would include analysis of large-sample behavioral data, surveys, and lab and field
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(yes, that means some museum and fieldwork!). Comparative analysis using advanced computational tools and wet lab techniques. Hands-on dissections of invertebrates for anatomical and physiological
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improve their financial outcomes. The projects would be developed and completed in collaboration with CBA. Primary methods would include analysis of large-sample behavioral data, surveys, and lab and field
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interpersonal and communication skills. While not a must, a strong background in computational methods and/or statistical methods is a plus. Special Instructions Applicants should submit a formal application and
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single synthetic program of computational geometry. Specific interests include morphology, design topology, discrete differential geometry, packings, and machine learning methods for unstructured geometric
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of the key criteria used when your application is evaluated. For projects that would be classified as basic research, we recommend applying for a Humboldt Research Fellowship. Should your programme search
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for program evaluation. We study the properties of the method in a large-scale public program in India using satellite images. Role of the predoctoral fellow: The fellow will be implementing our statistical