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- Zintellect
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Field
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strong interest in machine learning and deep learning algorithms · Research and publication experience in health outcomes research Application Materials: · CV · Cover letter · Contact information for three
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the possibility of yearly renewal subject to funding availability. Key Responsibilities • Conduct and lead research in 3D computer vision, deep learning, and AI for digital twin generation, publish findings in top
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experience in deep learning frameworks (TensorFlow/PyTorch) Experience with large-scale genomic/proteomic datasets and machine learning applied to biological sequences Knowledge of phylogenetics, protein
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highly stimulating environment that engages the best and brightest faculty and students to conduct deep and impactful research. Our faculty's research expertise and strengths cover several key
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Qualifications: PhD in experimental particle physics at the time of appointment. Preferred: Deep understanding of the particle detectors, particle identification, data analysis Machine learning experience is a
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and future climate scenarios—including counterfactuals—and integrating them with health surveillance and related data to estimate climate-attributable risk under Deep Uncertainty. The candidate will
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exceptional postdoctoral research fellows interested in developing deep learning and computational methods for pathology image analysis, multimodal data integration, and other medical modalities (e.g
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related field are particularly encouraged to apply.We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular
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Pharmaceutical health outcomes, (Pharmaco)epidemiology, Biostatistics, or a related field. · Expertise in or a strong interest in machine learning and deep learning algorithms. · Excellent communication skills
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related field. The ideal candidates will have experience in one or more of the following topics: deep learning for image and point cloud data processing, deep learning for time series data prediction