M. Madan Babu's Group at the Center of Excellence for Data-Driven Discovery and the Department of Structural Biology is seeking a highly driven, full-time human geneticist. This post will be part of a newly established Centre for Adipocyte Signaling (ADIPOSIGN) funded by a major grant from the Novo Nordisk Foundation. This position is available immediately with four years of funding. ADIPOSIGN is run by four partners with highly complementary expertise in the fields of adipocyte biology, transcriptional regulation, signal transduction, physiology, and computational systems biology:
Susanne Mandrup, Prof., Dept. Biochemistry and Molecular Biology, SDU, DK
Jan-Wilhelm Kornfeld, Prof., Dept. Biochemistry and Molecular Biology, SDU, DK
Madan Babu, Member, Dept. of Structural Biology SJCRH, USA
Zach Gerhart-Hines, Assoc. Prof., NNF Center for Basic Metabolic Research, University of Copenhagen, DK
The ideal candidate may have extensive expertise and a proven track record in statistical genetics, population genetics, computational analysis of human genetics and large-scale phenotype data and interpreting genome variation data (coding and non-coding variant effects). Your primary role as a computational human geneticist will be to identify, develop, implement and use computational genetics methods to integrate, mine, visualize and analyze multiple large-scale molecular omics and natural variation datasets for hypothesis generation, polygenic risk score calculation, and creation of molecular network models of obesity. You will develop computational pipelines and machine learning approaches to analyse genome sequences in order to quantify obesity risk and predict its effects. When submitting your application, please include a cover letter.
This position requires relocation to Memphis, TN. When possible, it will involve travel for collaborative purposes to Denmark. You will be working in a highly dynamic and collaborative environment, and will be expected to communicate complex informatics principles, methods, analyses and results to scientists from diverse backgrounds in the consortium. Excellent interpersonal and communication skills are a must, as you will interact directly with both Computational Biologists and Experimentalists in a highly interdisciplinary environment.
Ranked #1 in the U.S. in Pediatric Cancer, St. Jude Children's Research Hospital is a NCI-designated Comprehensive Cancer Center, and home to the Pediatric Cancer Genome Project. The St. Jude campus is a truly unique research environment, encompassing state-of-the-art HPC facilities, world-leading oncology basic research, outstanding Shared Resources and Core Facilities and a dedicated translational Chemical Biology and Therapeutics department. The $412M Advanced Research Center, opened in 2021, doubles the campus’ research space and adds support for an additional 1000 employees. There is a vibrant Data Science community at St. Jude, with opportunities to interact with researchers in the Biostatistics and Computational Biology Departments, and tremendous support from the Center for Applied Bioinformatics.
Preferred skills and experience:
- Experience with GWAS data analysis, QTL, eQTL analysis, and other methods of linking genotype and epigenetics information to phenotype
- Experience with development, implementation and application of Polygenic Risk Score estimation for phenotypes
- Experience with diverse statistical data analysis approaches (Bayesian and non-Bayesian statistical methods)
- Interest and expertise in adipocyte biology, human physiology, or other areas relevant to the study of obesity
- Interest or expertise in GPCR signalling, transcription, metabolic pathways, and/or nuclear receptors is desirable
- Experience with writing reports, developing web interfaces for data analysis and/or data dissemination.
- Experience with developing and maintaining databases (ideally graph databases)
- Experience with novel data visualisation methods
- To undertake cutting-edge research aimed at understanding the molecular basis of obesity phenotypes and how genetic variation contributes to the occurrence and development of obesity
- To develop and employ state-of-the-art computational genetics and genomics methods for the integrative analysis of whole genome, whole exome, transcriptome, miRNA, proteome and SNP genotype datasets within the framework of the ADIPOSIGN consortium
- To lead the analyses aimed at the identification of candidate genes and variants linked with obesity for further computational analysis and experimental validation
- To discover, develop and maintain network models of obesity and the effect of naturally occurring variants via a community database and make it available to the scientific community
- Establish computational pipelines and develop machine learning approaches to analyse genome sequences in order to quantify obesity risk and predict its effects
- To identify, develop and apply a broad range of techniques and data resources to pursue the research objectives
- To maintain collaborative interactions with all the other partners involved in ADIPOSIGN and provide general bioinformatics support for data analysis, interpretation and presentation of variation datasets
- To travel and present scientific work at seminars within the lab, consortium meetings, progress report meetings and at external meetings
- To collaborate with other members of Madan’s group and contribute to lab-wide discussions on developments in the area of computational genetics and genomics
- To draft scientific papers, and contribute to the overall preparation of research for publication
- To contribute to the mission of SJCRH and ADIPOSIGN in the public engagement of science, and the dissemination and translation of research findings into improvements in health care
- To assist in the training of PhD Students, post-docs and other members of the Group and ADIPOSIG
- Bachelor's degree is required.
- Master's degree or PHD is preferred.
- A PhD in computational human genetics or computational genomics preferred.
- If you have a PhD in statistical genetics, the ideal candidate preferably will have an extensive research experience working with human genetics datasets with a good molecular understanding of the basis of phenotypic diversity.
- Four (4) years of relevant experience is required.
- One (1) year of relevant experience may be acceptable with a Master's degree.
- Experience in programming (Python, Java, C/C++, Perl or other programming/scripting languages) under Linux/Unix environment is required.
- Experience with and the ability to deal with a wide range of users is required.
- Experience of data analyses and project management is required.
- Extensive expertise and proven publication history in human population genetics and statistical genetics preferred.
- Extensive experience in working with human genetics and genomics datasets preferred (e.g., 1000 Genomes project data, gnomAD, GTEX, Genomics England 100K Genomes Project, UK/FINNGEN/All of US Biobank datasets, etc.)
- Proven track record of experience in analysing diverse datasets (i.e., molecular and clinical data, different data types) and in interpreting and estimating the functional impact of variants (coding and non-coding) preferred.
- Experience in performing Genome-Wide Association Studies on novel genomic datasets, including data quality control, imputation, statistical study and data interpretation preferred.
- Good knowledge of omics datasets, molecular biology, systems biology and network biology approaches and methods preferred.
- Extensive programming in a general purpose or specialized statistical programming language such as Python and/or R preferred.
- Strong visualization skills as well as written and oral communication skills preferred.