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Field
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experimental research involving imaging systems, instrumentation, data acquisition, and quantitative validation is highly desirable. Familiarity with AI/deep learning methods for medical imaging is advantageous
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setup, validate results using electrophysiological recordings, apply deep-learning approaches to analyse noisy data, and disseminate your findings through publications. The team – You will work with the
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· Lead the design and implementation of mixture-of-experts neural architectures and reinforcement learning pipelines for counterfactual disease trajectory simulation for EMED, an NIH-funded multi-modal AI
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leadership, shared responsibility, and a deep commitment to genuine care and respect for our community. Find out more about our vision for a truly inclusive workplace in our Diversity, Inclusion and Belonging
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opportunities within the company. Responsibilities Develop and implement advanced computational and machine learning strategies, including deep learning, graph-based methods, and probabilistic modeling
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
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and management, machine and deep learning, as well as a solid understanding of wave phenomena and geophysical data analysis and imaging.
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | 2 months ago
field A strong publication record in high-impact venues (NeurIPS, ICML, ICLR, AAAI, ACL, EMNLP, Nature/Science family, JAMA, Lancet Digital Health, or equivalent) Deep expertise in machine learning and/or
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predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming, mathematics, physics. You will