St. Jude Children Research Hospital opening. The lab of Dr. Paul Geeleher is in the Department of Computational Biology at St. Jude Children’s Research Hospital. We are attaching the details of the Post doctoral Research Job below. Apply online. Latest Post doctoral Research Job
Role -Postdoctoral Research Associate – Computational Biology
Job location – Memphis, Tennessee
Req ID: JR755
The Geeleher Lab in the department of Computational Biology is seeking outstanding candidates for fully supported postdoctoral fellowships focused on developing innovative computational and statistical approaches to inform and improve therapies for pediatric cancer and other diseases.
The Geeleher Lab in the department of Computational Biology is seeking outstanding candidates for fully supported postdoctoral fellowships focused on developing innovative computational and statistical approaches to inform and improve therapies for pediatric cancer and other diseases. One of the premier pediatric research institutions in the world, St. Jude Children’s Research Hospital provides exceptional resources and a supportive environment for career development.
As a postdoctoral fellow, you will interact with leaders in cancer and translational research within and outside the institution and at national/international meetings. Innovative research will be supported by the lab’s infrastructure, which includes a wet-lab component. Postdocs have considerable flexibility and freedom to develop their research programs.
Current research areas
Developing machine learning approaches for integration of pre-clinical, clinical genomics and electronic health record data for drug re-purposing and pharmacogenomics of anticancer agents
- Identifying targeted therapies for patients with cancer is a central focus at St. Jude, and genome and RNA sequencing of patients’ tumors is now being performed regularly as part of standard-of-care. A major goal of the Geeleher lab is to explore machine learning approaches to prioritize targetable variants and expand the scope of targeted therapeutics.
- The postdoc will explore, optimize and build on emerging informatics techniques, including integrating somatic variation with transcriptomic variation and with protein-protein interaction networks. The Geeleher lab’s wet-lab component provides a platform for validation of computational predictions and discoveries. Successful completion of the project has the potential for a direct positive impact on patient care and high-impact publications.
Developing statistical methods for integrating single cell and bulk tissue expression data to understand the relationship between common inherited genetic variation, gene expression, and drug response
- Chemotherapeutic response is a complex trait influenced by numerous factors. Inherited genetic variants influencing gene expression (expression quantitative trait loci, or eQTLs) have been identified as major contributors. However, our recent work has shown that the degree of influence of eQTLs on gene expression in cancer is less well understood than previously thought.
- The postdoc will explore how inherited genetic variation influences cancer risk, disease progression and drug response, building on methods developed in the lab to deconvolute eQTL signals from bulk tissue expression data to specific cell types. This work will aim to improve our understanding of inherited genetic variation in cancer and yield computational approaches applicable to a broad variety of complex traits and diseases.
Eligibility and how to apply
- Candidates must hold a doctoral degree (Ph.D., M.D. or equivalent). Applicants with a Ph.D. in a quantitative field (computational biology, genetics/genomics, statistics, mathematics, computer science & related fields) are encouraged to apply. Strong candidates from a primarily wet-lab or clinical background who wish to develop sophisticated quantitative skills will also be considered. Successful candidates will have a track record of scientific productivity, e.g. a first author paper, or a demonstrable contribution to a large project. Experience working in pediatric cancer is an advantage but not necessary. Fluency in English and the ability to think and work independently are essential.
- The initial appointment will be for 1-2 years and can be renewed for up to a total of 5 years, depending on the candidate’s goals and qualifications.
- Interested candidates, please complete the online application and email your CV and cover letter to [email protected]. Please include areas of specific interest in a brief cover letter.
Greetings, all! We’ve compiled a set of interview questions and their corresponding answers to assist you in your preparation efforts. Dedicate time to study these resources attentively to enhance your performance during the interview for Post doctoral Research Job. Wishing you the best of luck with your preparations, and may you shine in your upcoming interview for Post doctoral Research Job.
Can you describe your experience in developing machine learning approaches for integrating genomics and electronic health record data for drug re-purposing and pharmacogenomics?
Sample Answer: In my previous research, I focused on developing machine learning models that combine pre-clinical and clinical genomics data with electronic health record information to identify potential drug repurposing opportunities and predict pharmacogenomic responses. I used various algorithms to integrate and analyze these data sources, ultimately aiming to enhance the precision of targeted therapies for cancer patients.
Could you provide an example of a challenging computational biology project you’ve worked on and how you addressed key challenges during the research process?
Sample Answer: Certainly. In one project, we aimed to prioritize targetable variants in cancer patients using machine learning. A significant challenge was integrating somatic variation data with transcriptomic variation and protein-protein interaction networks. To overcome this, we developed a novel approach that allowed for more accurate prioritization. This project required extensive collaboration with experts in both computational and wet-lab research, resulting in a successful integration and potential impact on patient care.
How do you plan to contribute to the ongoing research at the Geeleher Lab, particularly in the areas of machine learning and genomics integration for drug discovery?
Sample Answer: I am excited about the opportunity to contribute to the lab’s mission of improving therapies for pediatric cancer and other diseases. I plan to leverage my expertise in machine learning and genomics integration to further advance the development of computational approaches for drug discovery. This includes refining existing methodologies and exploring new avenues to enhance the identification of targetable variants and therapies.
Can you explain your experience in developing statistical methods to understand the relationship between genetic variation, gene expression, and drug response, particularly in the context of single-cell and bulk tissue data?
Sample Answer: In my previous research, I delved into understanding the complex relationship between genetic variation, gene expression, and drug response. I developed statistical methods to analyze single-cell and bulk tissue expression data and decipher the influence of inherited genetic variants on gene expression (eQTLs) in the context of cancer. This work aimed to improve our comprehension of these relationships and their implications for drug response, ultimately contributing to the field’s knowledge base.
How do you envision your research at the Geeleher Lab contributing to advancements in pediatric cancer therapies, and what impact do you hope to achieve with your work?
Sample Answer: My goal at the Geeleher Lab is to drive innovations in computational biology that directly benefit pediatric cancer patients. By developing advanced machine learning models and statistical methods, I aim to facilitate the discovery of more effective and targeted therapies. I believe that my work has the potential not only to improve patient care but also to lead to high-impact publications that contribute to the broader scientific community.