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time, we promote a supportive culture that fosters knowledge exchange, learning, creativity and scientific excellence. The PI, the group, the Department and university-wide initiatives (e.g., the Durham
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, biochemical, cell, and tissue biology method skills. Experience in using computational analysis (biostatistics, machine learning, data science, physics, or a related field). We value diversity and strongly
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in probability, stochastic analysis, and optimization. Applicants with knowledge in machine learning, as well as a track record of publications in top Actuarial Science, Mathematical Finance
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About the role We invite applications for a Postdoctoral Research Assistant to be part of a new research project ‘Innate and Learned Proficiencies in Facial Age Estimation’. The project is funded by
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operations is preferred, working knowledge of machine learning and artificial intelligence methods is highly desirable The successful candidate will demonstrate expertise in accelerator physics, accelerator
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platforms include GRO-seq, RNA-seq, ChIP-seq, ATAC-seq, CRISPR-seq, single-cell RNA/ATAC-seq, microC, machine learning, sophisticated mouse genetic tools, and an ex vivo tissue culture system from patients
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disseminate research results and data in peer-reviewed publications as well as presentations at international conferences. Required Qualifications: Education/Training- PhD in Electrical and Computer Engineering
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will work closely with Drs. Audrey Hendricks and Ryan Layer as well as grad students, postdocs, and other collaborators to develop and implement statistical and machine learning methods, software
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cross-sectionally. * Developing and applying machine learning and AI approaches to identify interactive topological relationships between various imaging modalities and multi-omics during aging and
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economics; food and agricultural economics; and international and development economics. Faculty members in the Dyson School frequently collaborate with colleagues in Computer and Information Sciences