Postdoc opening on developing advanced sparse machine learning and bioinformatics strategies

When was the last time you heard of a postdoc position advertized by somebody who is about to graduate. Wait no more, Zhilin who will have his thesis defense in a few days just sent me the following:

Hi ...Igor 

.....It's very nice to see that you have written a number of reviews on the progression of CS. I am waiting to see more.

Recently, one of my collaborative labs has a postdoc opening. The main job focuses on developing advanced sparse machine learning and bioinformatics strategies for multidimensional brain imaging genetics. The position description is copied below. The background on the medical stuff is not necessarily required. Like me, although I have no background on this field, I still successfully co-worked with that lab.

I would very much appreciate if  the announcement could be posted on your blog.

Best regards,

Zhilin

Here is the job announcement:

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.


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