22 algorithm-development-"LIST"-"Meta"-"University-of-Kent" positions at University of Washington
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figures of lab members. Candidate will train on the lab’s fundamental algorithms and run them in a collaborative manner with other team members to generate paper figures and make discoveries. Collaborative
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methodologies in brain diseases. The candidate will work on developing advanced new algorithms, testing and validation, and applications in these data modalities. The candidate will have the opportunity to work
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new code in python at PEP8 standards for solving problems toward creating neural connectome maps). Algorithm development initially will involve solving problems such as: (1) base calling of in situ
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, this research will contribute importantly to the discovery of novel risk genes, steer basic research and drug development, and advance personalized medicine. The successful candidate will also help with assisting
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established SOPs for new and existing molecular sequencing technologies, computational algorithms. Prepares and reviews papers on research. Develops new algorithms for analysis of RNA FISH, DNA FISH, and neural
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migration Developing appropriate statistical algorithms for updating model parameters estimates Working with database manager to organize the fish data and environmental covariates Analyzing data and
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or accelerated acquisition and reconstruction algorithms will be highly valued. Instructions Interested candidates should apply via Interfolio link with their CV (including a full list of publications), a
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development of optimized laboratory utilization algorithms. Consult and collaborate with existing clinical research units to support ongoing laboratory projects. Develop and implement department and division
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collaborative projects with other lab members and developing experimental protocols and experimental cell and molecular biology standards for the laboratory. The scientist will work on projects broadly
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. Proficient with running machine learning algorithms (e.g., Random Forest, CART) and regression models (e.g., SAR, LME) to derive ecological insights from big data sets. Experience developing reproducible and