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Field
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the financial sector and the economy at large. This role is ideally suited for those wishing to work in academic or industry research in quantitative analysis, particularly in the area of machine learning and
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position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
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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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Science, Materials Informatics, or a closely related discipline. 3–5 years of postdoctoral research experience with a strong publication record. Demonstrated expertise in machine learning for time-series or multimodal
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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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technologies like Azure Data Explorer (ADX), PostgreSQL, MongoDB, and data driven machine learning tools such as Spark, Power BI. Experience with optimisation, simulation and data analysis tools such as Matlab
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mathematical structures and computational algorithms underlying modern machine learning and artificial intelligence. Relevant themes include geometric and algebraic methods for learning, structure-preserving
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appointment). Strong background in statistical or machine learning methodology, optimization, or high-dimensional data analysis. Proficiency in R or Python; experience with deep learning, causal inference
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, or similar languages Background in machine learning or deep learning methods Knowledge of genomics, transcriptomics, evolutionary biology, and plant biology is an advantage Familiarity with large biological
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, implementing, and optimising advanced AI algorithms, with deep proficiency in machine learning architectures, scalable model development, and high-performance code. The role holder will have the opportunity