Job Listings

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 Title: The H5Nx Genomic Landscape: Predictive Modelling of Host and Antigenic Transitions A funded Postdoctoral Fellow opportunity is available in under the co-supervision of Dr. Zvonimir Poljak (University of Guelph) and Dr. Finlay Maguire (Dalhousie University). This is a full-time one-year position, with the possibility of extension, at the University of Guelph. This position is supported by the Canadian Institute of Health Research/Public Health Agency of Canada’s Avian Influenza One Health Research Funding Opportunity with contributions from NSERC.  

The graduate project focuses on the role of intrinsically disordered regions (IDRs) in plant-pathogen interactions. IDRs are protein regions that lack a stable 3D structure. IDRs are known to facilitate protein localization and protein-protein interactions, and proteins with IDRs often serve as functional hubs in regulatory and signaling networks. IDRs are also found in many pathogen secreted effectors proteins and it is believed that IDR domains are critical for the localization and function of these virulence factors. We are looking for a student to systematically identify IDRs in pathogen effectors and the proteome of the plant host. We will then use machine learning to predict the co-localization of these protein in the plant host cell. Ultimately, we will test these predictions in vivo to determine the role of IDRs in disease (this component of the project is outside the scope of the bioinformatics project, but will be available to the student if interested). The student will join the laboratory of David Guttman (Guttman Lab, PubMed, ‪Google Scholar‬) and work in collaboration with the Desveaux lab (Desveaux Lab). This study builds on an exciting and innovative project that has already resulted in numerous high-impact publications, including: The pan-genome effector-triggered immunity landscape of a host-pathogen interaction | Science Cooperative virulence via the collective action of secreted pathogen effectors | Nature Microbiology The effector-triggered immunity landscape of tomato against Pseudomonas syringae | Nature Communications Metaeffector interactions modulate the type III effector-triggered immunity load of Pseudomonas syringae | PLOS Pathogens The Arabidopsis effector-triggered immunity landscape is conserved in oilseed crops | Scientific Reports

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

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.

Ferlab | Génie logiciel Une équipe du CHU Sainte-Justine Au service des enfants atteints de maladies rares, cancers et autres conditions médicales graves, les applications et technologies que nous développons servent à optimiser l’utilisation des données massives de santé pour la recherche et ainsi contribuer à l’amélioration des traitements et diagnostics des petits patients du CHU Sainte-Justine et partout dans le monde. Développeur | Logiciel en bioinformatique Une carrière complètement différente Notre équipe s’agrandit !  Vous désirez mettre votre expérience en développement de logiciels [DM1] au profit de la cause et ainsi contribuer positivement à la société ? C’est dans une atmosphère d’entraide et d’harmonie que nous faisons avancer les mandats qui nous tiennent à cœur.

The UBC Michael Smith Laboratories at the University of British Columbia in Vancouver invites applications for a Research Associate (RA) position in Evolutionary Biochemistry to join the laboratory of Dr. Nobuhiko Tokuriki. The successful candidate will use microfluidic systems to perform single enzyme kinetics and high-throughput enzyme screening. The candidate will work on enzyme systems including alkaline phosphatase, phosphotriesterase, and APOBEC.      

Date: September 23, 2024   Postdoc/PhD positions in saRNA for Vaccine Development   We are seeking applications for multiple Senior Researcher positions, available immediately for exceptionally motivated Postdoctoral Fellows or PhD students. These positions offer a unique opportunity to contribute to a pioneering project applying artificial intelligence and machine learning to the development of RNA vaccines against emerging viral threats such as Influenza, Lassa, and Orthopox viruses.   Project Overview: This ambitious project utilises self-amplifying RNA (saRNA) technology, which offers the potential for more potent immune responses at lower doses compared to traditional mRNA vaccines. Our approach focuses on the use of structural RNA bioinformatics to design vaccine candidates and predict the effects of viral mutations, enhancing our ability to respond rapidly to evolving pandemic threats.

Our research group at York University, Toronto, requires someone with a bioinformatics background to assist with analysis of -omics datasets and in generation of highly professional publication-quality figures. The resulting data will be published as part of research manuscripts in scientific journals. Our laboratory conducts translational research studies on cardiometabolic disease. This involves fundamental research to investigate and identify new mechanisms responsible the development of cardiometabolic disease. Success of these approaches leads to identification of new potential therapeutic targets. In collaboration with pharmaceutical industry we conduct pre-clinical validation studies on potential new drug candidate(s). Our collaborative network includes research groups across Canada as well as in Hong Kong, Thailand, South Korea, USA and United Kingdom.

Mission or Goal of Unit: Virginia Commonwealth University (VCU) is a premier urban, public research university with a diverse student body in historic Richmond, Virginia. VCU enrolls more than 28,000 students on two campuses. The Center for Biological Data Science (https://cbds.vcu.edu) in VCU Life Sciences is a Chartered Research Center and focal point for bioinformatics instruction at VCU. The Center consists of 12 full-time, affiliate, and adjunct faculty and staff. The unit administers several academic programs, including a Bachelor of Science in Bioinformatics and Master’s degree programs for thesis (MS in Bioinformatics) and non-thesis (Professional MS in Bioinformatics) seeking students. Chief Purpose of this position in support of the above mission or goal. The Center for Biological Data Science is seeking applicants for the position of Assistant Professor (non-tenure eligible) whose expertise in bioinformatics will contribute to the teaching and service missions of the program. The core mission of the Center for Biological Data Science focuses on bioinformatics instruction and research. This is a unique and exciting opportunity for the selected individual to be part of a growing bioinformatics program and an interdisciplinary faculty.