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minimum of 12 months Appointment Start Date: Early/mid 2026 Group or Departmental Website: https://evodesign.org/ (link is external) How to Submit Application Materials: Please directly contact us at
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Vitro to In Vivo Extrapolation of Toxicant Effects on Ovarian Function” and will focus on phthalates and developing models that extrapolate in vitro assay results to predict in vivo effects on ovarian
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model biases, and identify sources of predictability. The project will involve; 1) rigorous interrogation of NOAA GFDL's CM4X simulation output with respect to coastal sea level variability and relevant
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Natural History. The researcher will develop deep learning models to predict individual bee age based on wing morphology. This model will be trained of existing wing images and applied to images of museum
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. Furthermore, a novel predictive algorithm of School-age neuropsychological outcome will be developed combining radiomic model of brain development, with qualitative neonatal MRI findings. Achievement
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dataset generation technique to optimize the training of neural networks (NNs) for seismic data prediction. The use of neural networks to predict seismic velocity models has shown increasingly accurate and
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with experimentalists to validate predictions made by their machine-learning models and drive wet-lab discoveries. The candidate may also have opportunities to work with research software engineers
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), multimodal vision and language models, and Large Language Models. Please find prior work here: (Google Scholar: https://scholar.google.com/citations?hl=en&user=oEifmSgAAAAJ&view_op=list_works&sortby=pubdate
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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technology. Development of cutting edge foundation models for protein design, small molecule property prediction, or protein function prediction Data generation and curation, including molecular simulation and