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of drugs, drug-like compounds, and their combinations. The team employs cutting-edge delivery strategies to enhance the efficacy and safety of both small and large molecules, thereby improving the risk
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discipline. Additional Qualifications Experience with running relevant numerical models (GCMs, mesoscale cloud-permitting or large eddy simulations) is desirable. Special Instructions Interested candidates
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opportunities will include grant writing, project management, advanced data analysis, scientific presentations, professional skills development, and career planning. Salary will be commensurate with NIH
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3 – Quantum algorithms for data clustering: Clustering algorithms are of fundamental importance when dealing with large unstructured datasets and discovering new patterns and correlations therein
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application of machine learning and AI methods to large-scale, longitudinal, routinely collected eRegistry data. The successful candidate will collaborate with researchers, PhD candidates, postdoctoral fellows
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with remote sensing data (satellite, aerial, hyperspectral, SAR, LiDAR) Computer Vision Natural Language Processing Remote Sensing Machine Learening and Deep Learning Reinforcement Learning Large
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. The successful candidate will investigate technical challenges and deployment risks associated with TES integration, and develop data-driven understanding of industrial energy use and storage
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approaches to large-scale biomedical data. Research topics may include foundation models for longitudinal electronic health records and genomics, multimodal learning integrating health records with molecular
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research assistants, fostering a productive and supportive lab environment. Analyze, interpret, and integrate large-scale datasets (e.g., genomics, transcriptomics, proteomics) to generate actionable
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: Design and implement ML, deep learning, and Large Language Models for orthopaedic applications Work with multimodal clinical data, including: Medical imaging (X-ray, CT, MRI) Electronic health records (HER