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Field
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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
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, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow'. We welcome you to join our community
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to the advancement of AI applications in biological sciences. This role presents a unique opportunity to work with pangenomic datasets while exploring the application of Large Language Models (LLMs) and machine
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of Informatics, Uni-versity of Oslo, and will be part of a growing research agenda at the intersection of epidemiology, statistical modeling, machine learning and public health data systems. The project aligns
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mathematical modelling tools. Excellent knowledge of programming languages such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in
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to undertake world-leading research in the design, integration and Edge-implementation/testing of multimodal machine learning models. Your experience in real-time implementation of federated AI and Edge-based