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on developing advanced machine learning models to quantify phenotypic traits of crops, including corn, soybean, and other selected species. These models will leverage data collected from various sources, such as
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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The Department of Biostatistics & Bioinformatics at Duke University invite applications for a postdoctoral associate position in mathematical modeling, as part of a new NIH-funded Center for Excellence in
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physics and cosmology. The research areas of interest include (but not limited to) the following fields(*1,*2): 1) Physics beyond the Standard Model and Cosmology axion physics, dark matter, flavor and CP
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statistical analyses on large databases Designing and running experiments Structural modelling and related econometrics Proving theoretical results Basic Qualifications Bachelor’s degree by start date (required
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and dexterity. Ability to work independently, as well as a team member. Prior experience with zebrafish or model organisms is a plus. Additional Information Appointment End Date: this is a one year term
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, bioengineering, or a related field, and have at least four years of experience working with tuberculosis, including non-human primate models and immunological approaches. Experience working in a Biosafety Level 3
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microenvironment, and other biological processes. Conduct preclinical studies using in vivo disease models (cancer, inflammation, organ injury, etc.) and new alternative methodologies (advanced biological models
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spectrometry; radiopharmaceutical dosimetry with novel agents and/or software techniques; alpha particle RPT radiation biology modeling and analysis of clinical trial data. For additional information on a subset
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include analyzing clinical and simulated data, developing pulmonary disease models, and manuscript preparation. The position offers opportunities for professional development, mentorship, and collaboration