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Fwd: [d-ssc] Postdoctoral fellow in statistical genomics of pain at McGill University in Montreal.






Begin forwarded message:

From: "Celia M T Greenwood, Dr" <celia.greenwood@mcgill.ca>
Date: September 18, 2019 at 2:27:01 PM GMT+1
To: 'd-ssc' <d-ssc@ssc.ca>
Cc: Yue Li <yueli@cs.mcgill.ca>, "Audrey Grant, Dr" <audrey.grant@mcgill.ca>
Subject: [d-ssc] Postdoctoral fellow in statistical genomics of pain at McGill University in Montreal.

Open position for a Postdoctoral bioinformatician in integrative statistical genomics of pain

 

Professors Yue Li and Audrey Grant are jointly hiring one postdoctoral fellow at the School of Computer Science and the Department of Anesthesia, Faculty of Medicine at McGill University. This research opportunity bridges statistical genomics methodological development with applied approaches directed towards chronic pain development, conditions featuring chronic pain, and pain sensitivity. Chronic pain is usually defined based on the persistence of pain experience for over three months of time and represents a substantial public health burden with a prevalence of 20 % in the general population. Although the genetic basis of various pain conditions (including fibromyalgia, migraine, lower back pain) is not well understood, the development of chronic pain implicates both the nervous system and immunity, and may be related to psychiatric outcomes. The overall goal of our project is to identify the molecular basis for the transition from acute to chronic pain, body site specificity vs. widespreadness across pain conditions, and pain sensitivity, and the successful candidate will define a focus within these areas according to their interests or initial findings. There is a lack of powerful statistical approaches scalable to large-scale population datasets that can consider multiple phenotypes (pain-related, immunological, neurological, psychiatric) and multiple types of genomic data (SNP genotypes, eQTL data, histone marks, etc.), simultaneously, to predict distinct pain-related phenotypes.

 

We seek a bioinformatician trained in the computational or statistical sciences at the postdoctoral level to start as of October 2019. The ideal candidate should display a passion for statistical and computational approaches applied to complex traits such as pain, and have the following skills:

 

Strong statistical genetics, computational biology or machine learning background;

Solid experience in at least one of the following programming languages or environments: R (using Rcpp), Python (Tensorflow), C++ (using Armadillo or Eigen library);

Familiarity with Linux, Shell scripts, job submission systems (e.g., qsub, bsub);

Fluent spoken and written English;

At least one English-language first- or co-first-authored publication;

Capacity for independent research.

 

This is a one-year project with possible extension for a second year. Through supervision and advice given by Prof. Grant and Prof. Li, the candidate will conduct comprehensive statistical analyses using existing and novel statistical methods developed or being developed in Prof. Li’s lab. The analytical tasks may involve:

Processing UK Biobank data and calculating the summary statistics of pain phenotypes (taking into account population admixture and various confounding covariates);

Statistical enrichment analysis of gene sets, pathways, cell/tissue-specific transcriptomes and epigenomes (harnessing reference single-cell and bulk public data);

Inferring mediating phenotypes of pain phenotypes within hierarchical polygenic models;

Statistical fine-mapping of pleiotropic sub-threshold functional genetic variants;

Linking regulator to target genes via in-cis DNA/RNA-binding data and in-trans eQTL information.

 

For the proposed computational approaches to be relevant to a clinical setting, a disease-focused applied science approach will be united with state-of-the-art computational expertise. Specifically, Prof. Grant (https://www.researchgate.net/profile/Audrey_Grant2) has expertise in genetic epidemiology and statistical genetics of immune-related phenotypes including asthma, and has drawn on the UK Biobank in her recent research. Prof. Li (https://www.cs.mcgill.ca/~yueli/) is an expert in machine learning and computational genomics. We offer a competitive salary and a comprehensive benefits package.

 

Please contact Prof. Yue Li (yueli@cs.mcgill.ca) or Prof. Audrey Grant (audrey.grant@mcgill.ca) for further inquiry about the position.

 

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