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11 days ago

PhD

Université du Québec à Montréal, Département d’Informatique -

Étudiant de doctorat en Informatique ou en Bioinformatique à l’Université du Québec à Montréal (Montréal, Canada)
Contexte du projet
La comparaison de séquences génétiques et génomiques est essentielle pour comprendre la diversité du vivant. Les arbres et réseaux phylogénétiques ont grandement contribué à notre compréhension de l’histoire du vivant. Cependant, l’augmentation exponentielle des séquences génétiques disponibles nécessite de nouvelles méthodes pour explorer la diversité des données évolutives.
Le projet est mené par une équipe multidisciplinaire de l’Université du Québec à Montréal (UQAM) (Prof. Vladimir Makarenkov), de l’Université de Sherbrooke (Prof. Guillaume Blanchet et Prof. Nadia Tahiri) et de l’Université de Montréal (Prof. Pierre Legendre).
Objectif du projet
Le projet utilisera des réseaux de similarité (ou graphe de similarité) où chaque nœud représente une séquence génétique. Les nœuds de ces réseaux sont connectés par des arêtes si leurs séquences montrent une similarité significative.
Ce type de structures en réseau doit permettre une analyse plus souple et complète des données génomiques et métagénomiques, surpassant les limitations des méthodes actuelles basées sur les arbres et réseaux phylogénétiques.
L’objectif principal de notre projet est de développer des méthodes informatiques et mathématiques pour analyser de grands jeux de données biologiques et bioinformatiques via des réseaux de similarité.
Le candidat doit posséder un diplôme de maîtrise en Bioinformatique, en Informatique ou en Mathématiques avec une bonne moyenne générale.
Rémunération: bourse non imposable de 29 000 CAD par année.

11 days ago

PhD

Université de Sherbrooke -

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

31 days ago

PhD

Canada's Michael Smith Genome Sciences Centre at BC Cancer -

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.

90 days ago

PhD

Memorial University of Newfoundland -

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.

154 days ago

PhD

Queen's University -

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: