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美国宾夕法尼亚州立大学招收生物信息学/进化基因组学博士生及博士后 已有1人参与
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The Huang Lab in the Biology Department at Pennsylvania State University (https://yifei-lab.github.io/) has multiple openings for prospective graduate students and postdoctoral scholars interested in bioinformatics and evolutionary genomics (see below). We welcome applicants from diverse academic backgrounds, including biology, bioinformatics, genomics, computer science, statistics, and other quantitative disciplines. Please feel free to contact Dr. Yifei Huang (yuh371@psu.edu) if you are interested in joining the lab. Ph.D. studentship: Multiple fully funded positions are available in Huang lab for prospective Ph.D. students. Ph.D. students in Huang lab will receive training in genomics, statistics, and machine learning to tackle fundamental questions in population genomics, human genomics, and molecular evolution. Prospective students are invited to send their CVs and transcripts to Dr. Yifei Huang (yuh371@psu.edu). We do not expect that students have already mastered both biology and computation before joining the lab. GRE is not required for graduate admission to Penn State. Postdoc position: An NIH-funded postdoctoral position is available immediately in Huang Lab. The postdoctoral scholar will develop and apply evolution-guided machine learning models to infer natural selection acting on genes and cis-regulatory elements. Potential applications of the research include, but are not limited to, prioritizing pathogenic variants and examining the genomic basis of human adaptive evolution. The ideal candidates will have a Ph.D. or equivalent degree, with relevant training in at least one field related to population genomics, comparative genomics, functional genomics, molecular evolution, statistics, and/or machine learning. Experience in analyzing large-scale population/comparative genomic data or implementing machine learning/statistical models is a plus. Interested candidates are invited to send CVs to Dr. Yifei Huang (yuh371@psu.edu). |
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2楼2021-09-13 22:17:34
3楼2021-09-30 10:05:11
