Bioinformatics Postdoctoral Position in Precision Medicine

Description:

Looking for the right person to join our team as we integrate multi-omic data and computational and AI approaches to identify treatment response profiles to immunotherapy using liquid biopsy!

Applications are invited for a Postdoctoral Fellow position at the New Brunswick Center for Precision Medicine, under the  supervision of Dr. Rodney Ouellette (M.D., Ph.D.) and Dr. Eric Allain (Ph.D.) of the newly formed Artificial Intelligence Magnified Precision Oncology (AIMPRO) Group. The postdoctoral fellow will work with multi-omic data from patient extracellular liquid biopsy with various methods in computational biology and bioinformatics. The postdoctoral fellow will be involved in projects that aim to understand the pre-treatment immune context in cancer patients receiving checkpoint inhibitors. Our primary goal is use bioinformatics and AI to understand the baseline profiles in patients and how these may predict treatment response/non-response. The candidate will also work with renown AI expert and collaborator Dr Moulay Akhloufi (PhD) of the Université de Moncton to employ machine learning algorithms on complex multisource patient sample data. Dr. Ouellette is the RR Leger Chair in Precision Cancer Research and has 25 years of experience in mentoring trainees at different levels. Most of his trainees successfully transition to academic, governmental, and private sector employment. Dr Allain is an emerging young researcher with an expertise in biology and bioinformatics. The successful candidates will work in a multidisciplinary environment and participate in research decision-making.  Findings will be presented in international conferences and peer-reviewed journals.

Responsibilities:

The candidate will partake in research projects involving expression profiles from multi-omics patient data sets to be analyzed using various computational and machine learning approaches. The postdoctoral fellow will be integrally involved in developing open-source computational pipelines for analyzing large-scale gene and protein expression data and predictive modelling for complex outcomes. In-depth knowledge of bioinformatics and machine learning methods will be an asset, as well as documented experience with differential expression, exploratory data analysis and developing computational pipelines. In addition, the postdoctoral fellow will be open to learning new emerging methods and techniques as opportunities develop.

Given the importance of scientific communication, the ideal candidate will have excellent data visualization skills and experience in using tools for reproducible research (R notebooks, git). The postdoctoral fellow should have strong written and oral communication skills, as demonstrated by peer-reviewed publications and conference presentations, and have the ability to work under deadlines independently or with general guidance. The collaborative nature of our research will require efficient communication with international collaborators who may be physicians, biologists, statisticians, and computer scientists. Lastly, our labs support the training and mentorship of young trainees; therefore, the postdoctoral fellow will be expected to assist in training new trainees.

Qualifications:

REQUIRED QUALIFICATIONS

• Ph.D. (or similar qualification) in Bioinformatics, Biostatistics, Computer Science, computational sciences, relevant field, or a combination of relevant experience and education.
• Proficiency working with transcriptomics datasets and assays, including RNA-Seq, sRNA-Seq, or spatially resolved transcriptomics.
• Must have proficient computational skills, including a high-proficiency in python or R, using UNIX-like OS, and high-performance computing.
• Experience with computational methods development and application
• Experience with machine learning techniques, such as random forest models, support vector machines, and other supervised or unsupervised approaches.
• Ability to work independently and collaboratively as a member of an interdisciplinary team.
• Ability to multitask and manage projects
• Strong verbal and written communication skills

PREFERRED QUALIFICATIONS

• Experience managing large and complex datasets
• Experience with supervising students, mentoring, or teaching