Canada’s Michael Smith Genome Sciences Centre (GSC)

Today’s Research. Tomorrow’s Medicine.

The GSC is a department of the BC Cancer Research Institute and a high-throughput genome sequencing facility. We are leaders in genomics, proteomics and bioinformatics in pursuit of novel treatment strategies for cancers and other diseases.

Among the world’s first genome centres to be established within a cancer clinic, for more than two decades our scientists and innovators have been designing and deploying cutting-edge technologies to benefit health and advance clinical research.

Among the GSC’s most significant accomplishments are the first publication to demonstrate the use of whole-genome sequencing to inform cancer treatment planning, the first published sequence of the SARS coronavirus genome and major contributions to the first physical map of the human genome as part of the Human Genome Project.

By joining the GSC you will become part of an exceptional and diverse team of scientists, clinicians, experts and professionals operating at the leading edge of clinical research. We look for people who share our core values—science, timeliness, respect—to join us on our mission to use genome science for the betterment of health and society.

Summary

Job Reference No. RA_R00006_Clinical_2025_01_10

Canada’s Michael Smith Genome Sciences Centre (GSC) of the BC Cancer Research Institute is a state-of-the-art, large-scale, high-throughput, clinically accredited genomics and bioinformatics facility located in one of the most vibrant and diverse cities in the world.

As a Research Associate within the Centre for Clinical Genomics Informatics team at the GSC, you will play a pivotal role in advancing clinical bioinformatics capabilities by developing, validating, and optimizing workflows and pipelines to support cutting-edge genomic technologies. The Research Associate will report to the Team leader and is anchored within a team of exceptional computational scientists, programmers and clinical researchers, who collaborate directly on the development and maintenance of robust, cost efficient, and competitive clinical genomics pipelines.

This is an opportunity to work with highly motivated colleagues in a science-oriented, creative and dynamic environment. We offer a competitive salary, excellent benefits and significant career development opportunities.

This position is initially funded for two years.

PhD student to start in January 2025 or as soon as possible to join a multidisciplinary team with several groups involved including MUN’s Centre for Innovation and Learning in Teaching (CITL), College of the North Atlantic (CAN), and Nova Scotia Community College (NSCC). Student will be working under the supervision of Dr. Gagnon (http://www.ucs.mun.ca/~pgagnon/) and Dr. Peña-Castillo (https://www.cs.mun.ca/~lourdes/).

Project description

Using the large-scale mapping of kelp beds provided by other collaborators, we will establish ground truth (labelled) regions, delineating areas where kelp beds exist (true positives) and where they are absent (true negatives).  These labelled regions, indicating whether kelp beds are present, will be the basis for a self-supervised deep learning approach. This approach will allows us to train deep learning architectures for kelp bed detection using unlabelled satellite images. Beyond the initial  self-supervised training phase, we will implement an active learning  framework. Once an accurate kelp bed detector is generated through this approach, we will apply the classifier to satellite images spanning multiple years to track changes in kelp beds over time, allowing us to  monitor the effects of intervention programs and assess the impact of climate change on kelp bed dynamics. By extending our classifier to temporal datasets, we aim to contribute valuable insights into the dynamics of kelp ecosystems in the face of a warming climate and human interventions.

Additionally, we will develop advanced visualization tools that enhance the interpretation and presentation of satellite remote sensing data. This involves creating interactive, user-friendly interfaces that enable researchers, policymakers, and the public to engage with complex spatial data in a more intuitive and insightful manner. By utilizing technologies such as 3D mapping, augmented reality (AR), and virtual reality (VR), this project aims to transform traditional two-dimensional data representations into dynamic, multi-dimensional visual experiences. This will not only aid in better understanding spatial relationships and patterns in the data but also facilitate more effective communication of findings to a broader audience, thereby making satellite data more accessible and actionable for decision-making processes.

This fellowship (offering $35K-$40K annually for four years) is offered to work on developing computational algorithms in cancer and epigenetics. The potential projects range from data analysis to developing deep-learning algorithms. Learn more about our research at:

https://panchenko-lab.org/

Available PhD programs:

• Pathology PhD Program
• Computer Science PhD Program
• Biochemistry & Molecular Sciences PhD Program

Project Title: Polygenic Risk Score development for chronic low back pain

Professors Carolina Meloto and Audrey Grant are hiring one PhD student at the Faculty of Dental Medicine and Oral Health Sciences at McGill University. This research opportunity is focused on applied approaches directed towards prevention of chronic low back pain (cLBP) development. Broadly, chronic pain is defined based on the persistence of pain experience for over three months and represents a substantial public health burden with a prevalence of 20 % in the general population, with cLBP as the most common chronic pain condition. Accurately predicting individuals who are at risk of cLBP is a vital step needed to enable cLBP prevention strategies. Despite cLBP having a sizable genetic heritability, models proposed to predict cLBP development are based on biopsychosocial measures and do not incorporate genetic variability. Here, we will capitalize on large scale biobanks available to our teams to derive and assess the performance (discrimination, calibration, and accuracy) of a polygenic risk score (PRS) that predicts cLBP development. We plan to use cutting edge methodology and new data resources to maximize predictive performance of the PRS.

Within this project funded by the ADlab laboratory (Arnaud Droit Lab, compbio.ca), the main goal will be to transform the access and analysis of biological knowledge databases (BKDs) through the integration of these databases into a knowledge graph and the use of large language models (LLMs). This initiative aims to facilitate natural querying of biological data, making research more accessible to biologists without specific expertise in programming or data analysis. The main objectives include:

• Building a biological knowledge graph that integrates multiple BKDs to improve their cohesion and extract valuable information.
• Developing and training specific LLMs capable of interpreting and querying this graph in natural language.
• Creating and evaluating a conversational agent based on these LLMs, to allow intuitive querying of BKDs using natural language requests.