*Postdoctoral associate sought for developing statistical models for
imaging-genetic data*
*Job description*
We are looking for a self-motivated, quantitative scientist with a strong
background in applied statistics and machine learning who is genuinely
interested in working with Big Data in Healthcare, specifically
imaging-genetics. The objective of this project is to develop a statistical
model relating population genetics and neuroimage analysis. For example, to
characterize the underlying hidden population structure for mental diseases
such as Schizophrenia. We have unique large-scale datasets consisting of
thousands of subjects each with genotype, brain image measurements, and
neurocognitive scores. The position will focus on developing a general
statistical model that incorporates information from multiple sources of
public and private data and across different modalities of data (imaging,
genetic, gene expression, etc) to infer causal relationship between gene
and abnormal variations in brain structure. The project is a
multi-disciplinary research collaboration between multiple universities and
industry with high potential for high impact publication. The candidate
for this position is expected to have strong background in machine learning
advanced statistical modeling and data analysis and possess excellent
writing and communication skills. Having experience in biostatistics and
particularly analyzing genetic and imaging data in a big plus.
Responsibilities include development and implementation of the algorithms,
data management, statistical data analysis, collaboration with scientists
in multi-disciplinary teams, and presenting findings at the international
conferences.
*Education:*
PhD in bio-/Statistics, Computer Science, Machine Learning, Electrical
Engineering or related fields, with research experience in statistical
machine learning using complex multivariate data.
*Technical skills:*
*Required*
● Experience with:
○ Bayesian data analysis or graphical model development and evaluation
or
○ Knowledge of machine learning algorithms with robust feature
selection and optimization
● Expert level knowledge:
○ of at least one scientific computing languages such as
R/Python/MATLAB
or
○ of low-level languages such as C/C++
● Genuine interest in learning new concepts from biology and more
specifically genetics.
*Preferred*
The following experience are preferred but not necessary
● Hands-on experience with statistical population genetic and
epidemiology
● Experience with causal inference and related statistical concept and
tools
● Hands-on experience with neuroimaging analysis softwares such as
FreeSurfer/AFNI/SPM
● Expert knowledge of Unix environment and scripting
● Experience with High-performance computing and cloud computing.
The position is supported for 2 years. However, candidates will be
appointed for one year, with a second year extension possible based on
progress. The primary appointment of the postdoc is with the Department of
Biomedical Informatics in the University of Pittsburgh, with an option to
hold a guest position at Carnegie Mellon University. Interested applicants
should directly contact Dr. Kayhan Batmanghelich: [email protected].
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