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Program
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data collection and management Data analysis and model building Develop advanced deep learning and machine learning algorithms. Assist with organizing large-scale multimodal neuroimaging dataset, brain
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management of databases for cohort-based epidemiological, clinical, genomic and transitional studies. Consult on design and development of multiple data analyses, including data availability, choice of metrics
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applications for a Postdoctoral Researcher position. The selected candidate will undertake both collaborative and self-directed research on mathematical modeling, optimization and simulation models/algorithms
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work with Ayan Paul at EAI and is expected to develop AI algorithms for drug discovery with a combination of public and proprietary data, develop AI algorithms for variant effect prediction with models
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algorithms and software for data analysis. Responsibilities include querying databases, data processing, supervised and unsupervised machine learning, deploying production models, and communication
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models, signal processing methods, artificial intelligence (AI) tools, and optimization algorithms grounded in electromagnetic (EM) principles. The project is inherently interdisciplinary, bridging
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learning on large-scale HPC systems Scalable and energy-efficient AI training algorithms Image reconstruction, segmentation, and spatiotemporal modeling High-performance computing for large-scale AI and
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spanning multiple locations and entities, where complex constraints and resource interdependencies – among people, machines, and robots – demand the deployment of intelligent algorithms for orchestration
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Engineers. Serve as liaison with Princeton Research Computing staff on GPU cluster related issues. Professional Development Learn the underlying science, mathematics, statistics, data analysis, and algorithms
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with signal processing and control algorithms. Excellent communication skills in English (written and spoken); ability to work in interdisciplinary teams and to publish scientific results. A strong