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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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://www.nature.com/articles/s42256-021-00407-x), and extending these tools or developing new models as needed. The candidate will have the opportunity to work directly with experimentalists to validate predictions and
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-omics analysis, systems modeling, and predictive simulation of host-microbiome-driven disease processes Translation of microbiome and systems-level discoveries into clinically actionable biomarkers
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stem cell models from patients with Parkinson's disease to understand the underlying causes of neurodegeneration and to develop biomarkers and new therapies for Parkinson's disease. http
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thermomechanical processing and develop computational models to predict mechanical properties, their evolution, and formability. Through August 2026, contribute to the DOE ARDAP research project by evaluating
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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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to predict thermal runaway on the cell level. The combustion and gas model developed on the cell level will then feed into the work to accurately predict thermal runaway on pack, module, and system levels
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learning, generalization/robustness and privacy aspects in scalable learning algorithms. Large‑scale optimization and control: Optimal control, model predictive control and other optimization‑based control
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data to identify proteomic signatures and develop novel predictive models for Alzheimer’s, Parkinson, and Dystonia as well as to identify novel proteins and pathways implicated on disease pathogenesis
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a