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Perrimon’s group in the Program of Genetics at Harvard Medical School. Perrimon lab is actively generating data sets of omics scale and this position will involve working with other post-doc trainees in
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, economics, and genetics, and methodological expertise from educational measurement, psychometrics, econometrics, statistics, and biostatistics. The Postdoctoral fellowships are affiliated with CREATE’s
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optimisation algorithms for quantum routing using genetic algorithms (GA), ant colony optimisation (ACO), and particle swarm optimisation (PSO), optimising cost functions subject to entanglement fidelity
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of themselves. The candidate will organize, manage, and curate big data on the cluster and find biological patterns in a wide range of genetic and epigenetic sequencing and imaging data to facilitate the paper
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of malignancies, blood disorders, and experimental therapies. Job Summary The Data Science, Analyst will will work to maintain and deploy algorithms for accurate detection, segmentation, and classification of cells
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simulation software. Develop algorithms and techniques that reinvent signal understanding and processing. Collaborate closely with the tight-knit members that make up the Simulation Team and collaborate
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learning algorithms for a variety of predictive analytics research projects. Coordinates data collection, econometric analysis and provides quality assurance for research projects. Contributes to research
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via bitbucket). Backup code on bitbucket and oversee the revision of the code to integrate with other algorithms. Algorithm development initially will involve solving problems such as: (1) base calling
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, the design of end-to-end algorithmic workflows, assessment of methodological trade-offs and challenges, and the implementation of NLP systems using high-level programming languages. The successful candidate
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 5 days ago
data structures, algorithms, and research workflows 2. Assisting with DevOps and automation practices, including continuous integration pipelines and deployment workflows 3. Supporting improvements