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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
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for biotechnology. By combining mecha-nistic, statistical, and machine-learning models with automated experimental execution, the project will enable traceable, reproducible, and metadata-rich experimental planning
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, and training and benchmarking of language models. The research profile of the group is heavily machine-learning oriented, and the group has access to excellent HPC infrastructure. For more information
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Your Job: Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids Apply machine learning/AI or surrogate modeling (e.g., neural networks
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, certification, and/or registration. Specific knowledge, skills and abilities required to perform the job satisfactorily include: Demonstrated experience developing and/or implementing machine learning models in
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team members. Learn more at: https://hr.duke.edu/benefits/ Equal Opportunity Employer: Duke is an Equal Opportunity Employer committed to providing employment opportunity without regard
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or similar) Preferred Qualifications: Experience with MRI/fMRI/DTI, PET, multimodal fusion, and/or machine learning Strong programming skills (Python/MATLAB), version control, and HPC workflows Special
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Informatics, Health Data Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated
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data solutions. Expert knowledge of data modeling, statistical analysis, machine learning, and optimization techniques. Expert in leading and executing complex, high-impact data analytics projects and
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of user support services, including network support, training, and computer desktop support. This role involves end-user support and training, focusing on endpoint technologies (desktops, laptops