2,研究工作:这个职位的主要工作是:develop advanced sparse machine learning and bioinformatics strategies for multidimensional brain imaging genetics。生物信息学方面的背景并不是非要求有不可(来了以后可以逐渐了解一点;当然有是最好了),申请这个职位更重要的是要有机器学习,多元统计方面的背景等等。我和他们合作了一年多,我自己也没有任何生物信息学方面的背景,不过还是很成功的和他们合作了许多工作,不少工作已经发表或者正在审稿中。最下面有5篇相关的文章供大家参考。不过我建议最好发email详细询问。
Applications are invited for a Postdoc Position in the Imaging
Genomics Lab at the Indiana University School of Medicine (IUSM), funded
by an NIH R01 grant. The project is focused on developing advanced
sparse machine learning and bioinformatics strategies for
multidimensional brain imaging genetics.
Requirements include a
Ph.D. in computer science, informatics, statistics, or related
disciplines, and a record of academic productivities. Preference will be
given to candidates who have experience with advanced techniques for
analyzing genome wide array data, complex phenotypic data, and/or
systems biology data. A strong interest in integrative analysis of
multimodal neuroimaging data, high throughput omics data, and other
biomarker data, would be highly desirable, as would solid background in
machine learning and bioinformatics, and strong programming experience
using Matlab, R, Python, and/or C/C++.
The Imaging Genomics Lab
(http://www.iupui.edu/~shenlab/) is affiliated with two
multidisciplinary centers at IUSM: (1) Center for Neuroimaging, the hub
for all neuroimaging research activities on campus, and (2) Center for
Computational Biology and Bioinformatics, the bioinformatics core of
IUSM. There is an excellent set of critical resources at IUSM, including
(1) experts from neuroscience, imaging science, computer science,
genetics, informatics, and statistics, (2) state-of-the-art imaging
facilities, and (3) large scale computer systems and advanced software
tools. The successful candidate will benefit from mentorship of a
diverse research team and will be exposed to cutting-edge technology by
collaborating on various genomic and imaging projects.
Interested candidates should email their CV, selected reprints and a list of three references to: Li Shen at shenli@iupui.edu
Indiana University is an AA/EOE employer, M/F/D.
The approaches will be similar to those proposed in the following papers.
[1]
Vounou M, Janousova E., Wolz R., Stein J. Thompson P., Rueckert D. and
Montana G. (2011) Sparse reduced-rank regression detects genetic
associations with voxel-wise longitudinal phenotypes in Alzheimer's
disease. NeuroImage, 60(1):700-716
[2] T. Ge, J. Feng, D.P.
Hibar, P.M. Thompson, and T.E. Nichols. Increasing power for voxel-wise
genome-wide association studies: the random field theory, least square
kernel machines and fast permutation procedures. NeuroImage, 63(2):
858-873, 2012.
[3] Witten DM, Tibshirani R, and T Hastie (2009) A
penalized matrix decomposition, with applications to sparse principal
components and canonical correlation analysis. Biostatistics 10(3):
515-534.
We will be dealing with data similar to those in the following papers.
[4]
Meda SA, Narayanan B, Liu J, Perrone-Bizzozero NI,Stevens MC,Calhoun
VD, Glahn DC, Shen L, Risacher SL, Saykin AJ, Pearlson GD (2012) A large
scale multivariate parallel ICA method reveals novel imaging-genetic
relationships for Alzheimer's disease in the ADNI cohort. Neuroimage,
60(3):1608-1621. doi:10.1016/j.neuroimage.2011.12.076
[5] Shen L,
Kim S, Risacher SL, Nho K, Swaminathan S, West JD, Foroud TM, Pankratz
ND, Moore JH, Sloan CD, Huentelman MJ, Craig DW, DeChairo BM, Potkin SG,
Jack CR, Weiner MW, Saykin AJ, and ADNI. Whole genome association study
of brain-wide imaging phenotypes for identifying quantitative trait
loci in MCI and AD: A study of the ADNI cohort. NeuroImage,
53:1051-1063, 2010. http://dx.doi.org/10.1016/j.neuroimage.2010.01.042.