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EliCat鐵蟲 (初入文壇)
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美國弗吉尼亞理工(Virginia Tech) ECE 機(jī)器學(xué)習(xí)/人工智能/數(shù)據(jù)科學(xué) 2022Fall 全獎博士
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The Yu lab at the Bradley Department of Electrical and Computer Engineering at Virginia Tech currently has 6 immediate openings for doctoral graduate research assistantship (GRA) in the areas of machine learning, artificial intelligence, and data science, with applications to biomedical and especially brain-related problems. The electrical and computer engineering graduate program at Virginia Tech is currently ranked the top 18th in the US, with 81 full‐time tenured or tenure‐track faculty members and 4 members of the National Academy of Engineering. More details about our department can be found at https://ece.vt.edu/about/metrics.html. We are seeking highly qualified and motivated candidates who are interested in truly scientific research career (extending math and engineering into life sciences) and inspired by his/her own curiosity about the process of discovery. Our research is focused on the frontiers in: (1) statistical modeling and pattern analysis of genome-wide high throughput genomic and molecular data; (2) modeling and tracking of biological objects in microscopic video; (3) quantification and analysis of brain activity based on time-lapse microscopic functional imaging data; and (4) mathematical modeling of astrocyte’s functional role in neural circuits. Candidates are expected to have degree in electrical/electronic engineering or closely related areas, e.g., computer science, applied mathematics, automation, biomedical engineering. If you are interested, please submit your application before Jan.15th, 2022 at the website, https://graduateschool.vt.edu/admissions/how-to-apply.html. Feel free to contact Prof. Guoqiang Yu at yug@vt.edu to express your interest and get more information. The Yu lab is located at one of the three largest biomedical research clusters in the US (Metropolitan Washington, DC). The Yu lab is an official member of NIH BRAIN Initiative Consortium and NIH Data Science Consortium. We have established active collaborations with JHU, Harvard, Stanford, UCSF, UC Davis, Duke, University of Michigan, Salk Institute, and HHMI Janelia Research. We not only apply the existing computational techniques to solve biomedical problems, but also develop advanced machine learning methods inspired by the cutting-edge problems. You can have a look at our work at GitHub webpage, https://github.com/yu-lab-vt. A few representative recent papers from our group are listed below. AQuA, published at Nature Neuroscience, link: https://www.nature.com/articles/s41593-019-0492-2 CINDA, published at IEEE TPAMI, link: https://ieeexplore.ieee.org/document/9204816 muSSP, published at NeurIPS, link: https://proceedings.neurips.cc/paper/2019/file/b1d10e7bafa4421218a51b1e1f1b0ba2-Paper.pdf SynQuant, published at Bioinformatics, link: https://academic.oup.com/bioinformatics/article/36/5/1599/5584198 ConvexVST, published at ICML, link, https://proceedings.mlr.press/v139/wang21p.html |
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