Jobs

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.

Project Background.
We are seeking a motivated PhD student to join our research team in developing advanced methods for phylogenetic network analysis, with a specific focus on network consensus algorithms. Significant progress has been made in consensus tree methodologies. A consensus tree is a phylogenetic tree that synthesizes multiple phylogenetic trees, each with the same leaf labels but possibly differing topologies. These trees are often generated through bootstrapping or other sampling techniques. Traditional approaches to consensus tree construction focus primarily on topological aspects, often overlooking the importance of branch length, which captures the temporal progression of genetic mutations. However, in the context of consensus networks, very few studies have introduced relevant concepts.

Project Objective.
Our project addresses this limitation by integrating branch-length data not only into the construction of consensus trees but also into network consensus construction. This more comprehensive approach aims to provide a richer and more accurate representation of evolutionary relationships by combining topological structure, branch frequency, clade frequency, and branch length.

The candidate must hold a Master’s degree in Mathematics, Computer Science, or Bioinformatics with a strong overall GPA.
Compensation: non-taxable scholarship of +20,000 CAD per year.

To apply, please send your CV, list of peer-reviewed articles (optional), and a cover letter to: Prof. Nadia Tahiri

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.