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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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illness. We have a large team working on developing technological solutions for these applications. We are seeking a computer science researcher to take an active role in developing novel machine learning
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AI/machine learning, and data analysis using MATLAB or Python. Provide technical assistance on related research projects, such as preparing progress presentations and reports for funding agencies
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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 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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updates in a landscape evaluation toolkit, which include new models for listed and sensitive species. These models can inform managers during planning on how to improve forest health and reduce adverse
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decision tool based on SLP/NLP, and utilizes large language models. The project will focus on interaction with clinicians, with a goal of closing the gap between foundational research in machine learning and
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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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are essential. Preferred Qualifications: Prior experience with machine learning (ML) in high-energy physics is highly desirable, though not required. Appointment Details: The position is expected to be based
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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