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a track record in computational modelling that explores the dynamics of AI systems and the development of autonomous AI agents, experience with machine learning, reinforcement learning, and generative
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SUMOylation, transcription factors, or chromatin dynamics. Expertise in machine learning or statistical modeling for biological data. Knowledge of enhancer-promoter interactions and 3D genome organization. All
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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in machine learning, computer vision, and medical
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research-operational partnerships and learning about systems involving forest fuels and fire emissions modeling. They will gain experience with modeling, coding, and database management in support of a
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data, spatial modelling, multivariate statistics and/or machine learning, and relevant coding languages (e.g. R, Python), including a sound understanding of FAIR data principles, data management and
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consequences to sensitive species. Revisions also include updating values in the existing models that reflect new science and lessons learned over the last 15-years. The toolkit also includes evaluation tools
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from date of hire. Preferred Qualifications Aptitude and experience with: (a) predictive machine and deep learning techniques, (b) statistical analysis, (c) hands-on experience using models such as
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and machine-learning methods (AI/ML) to extract novel biological insights that drive our translational and fundamental research programmes. In addition to your research leadership, you will play a
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position under the supervision of Dr. Chris Smith Home | Chris Smith . Our new lab in Evolution, Ecology, and Behavior investigates machine learning approaches for population genetics. Our research