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About OICR OICR is Ontario’s cancer research institute. We bring together people from across the province and around the world to improve the lives of everyone affected by cancer. We take on the biggest challenges in cancer research and deliver real-world solutions to find cancer earlier and treat it more effectively. We are committed to helping people living with cancer, as well as future generations, live longer and healthier lives. Launched in December 2005, OICR is an independent institute funded by the Government of Ontario through the Ministry of Colleges and Universities. Job Details Position: Bioinformatician II Location: MaRS Centre, Toronto Department: Computational Biology (Genome Sequence Informatics) Reports To: Senior Manager Salary: Commensurate with level of experience; total compensation includes a competitive benefits plan, plus a defined benefit pension plan (HOOPP) Hours: 35 hours/week Job Type: Hybrid Status: Full-time, Permanent Position Summary The Ontario Institute for Cancer Research (OICR) is seeking two (2) experienced and passionate Bioinformaticians to join the Genome Sequence Informatics (GSI) team at OICR. The Bioinformatician functions in a junior to intermediate technical role that requires an individual with proven skills in both biology and computing, and who is dedicated to supporting a multi-disciplinary team of scientists, laboratory technicians, and informatics professionals. GSI is part of the Genomics program at OICR (https://genomics.oicr.on.ca/) and supports the sequencing teams that manage clinical and research projects. GSI designs applications to streamline and automate analysis, manage the data lifecycle, and create useful and dynamic reports at scale. We ensure that data flows smoothly, securely and correctly from the lab through to the clinicians and researchers who use it. We use languages and software tools like Java, Python, Perl, Javascript, OpenStack, Open Grid Engine, Univa, MySQL and PostgreSQL. The main areas covered by GSI are: Lab tracking: Develop the open source MISO LIMS (https://miso-lims.github.io) augmented by other applications. Pipeline/Data management: Develop and run workflow systems like Vidarr (https://oicr-gsi.github.io/vidarr/) and Cromwell (https://cromwell.readthedocs.io) to automate and streamline data analysis, tracking, and workflow management. Data release: Delivery of sequencing and analysis, archiving of data for long term storage and deposition of data to public archives. Reporting: Maintain of a suite of specialized reports for quality control, forecasting, and lab operations, and to accompany data delivery. Analysis: Development of analysis workflows to run in our pipeline system, and development of tools supporting analysis tracking and data management. Conducting project-specific analysis on sequencing data with an emphasis on cancer genomics, both for internal research projects and for collaborators. Cancer Genome Interpretation: Analyze and interpret genomic data using accredited pipelines and processes for clinical purposes.

Are you passionate about unraveling the secrets of RNA and deciphering their functional structures? Do you have a strong background in bioinformatics and a burning curiosity to explore the dynamic world of RNA molecules? If so, we invite you to embark on an exciting research journey with us! 🔬 About Us: At the Computational Biology Research and Analytics (COBRA) Lab at the University of Alberta, we are at the forefront of cutting-edge RNA research. In collaboration with Dr. Lara Mahal (Professor of Chemistry and Canada Excellence Research Chair in Glycomics) we are seeking a highly motivated and talented postdoctoral researcher to join our team of passionate scientists. Our mission is to advance our understanding of RNA biology and its implications in various biological processes. 🧬 Position Overview: – **Position**: Postdoctoral Researcher in RNA Bioinformatics – **Location**: Edmonton, Alberta – **Duration**: One year with possibility of extension – **Start Date**: As soon as possible   Why Join Us? – Work in a dynamic and collaborative research environment. – Access cutting-edge technology and resources for your research. – Opportunities for career development and networking. – Competitive salary and benefits package ($70,000 per annum). Join us in unraveling the mysteries of RNA and making a significant impact in the field of molecular biology. Apply today and become an integral part of our innovative research team!

We seek a postdoctoral fellow to help develop an AI-powered self-driving lab to automate multiscale mapping of multicellular, human biological model systems. This fellowship provides a unique opportunity to work at the intersection of biology, engineering, and artificial intelligence, contributing to the future of precision medicine through innovative approaches. The successful candidate will play a key role in advancing machine learning-driven analysis of high-content imaging data and integrating multi-omics datasets within human organ mimicry systems. This work will involve developing novel computational approaches for biological discovery while engaging in a highly interdisciplinary research environment.  This is an Acceleration Consortium Post Doctoral Fellowship supervised by Staff Scientists Dr. Ilya Yakavets and Dr. Yimu Zhao in the Human Organ Mimicry self-driving lab (SDL) with a direct reporting line to co-supervisor Professor Gary Bader in the Departments of Computer Science and Molecular Genetics at the University of Toronto.  The Human Organ Mimicry SDL is an autonomous, AI-driven lab focused on advancing material development, drug discovery, and personalized medicine. By integrating organoids, organ-on-chip technology, biosensors, and machine learning, the lab creates biomimetic models that closely replicate human organ functions and generate clinically relevant data. These models enable medium-throughput, high-content experimentation, accelerating data-driven health research.  The position provides the chance to contribute to cutting-edge research within a vibrant intellectual community at Canada’s leading university. It offers access to state-of-the-art facilities and an outstanding research environment at the Acceleration Consortium within the University of Toronto, working within a world-class team of scientists dedicated to advancing machine learning applications in human organ mimicry, precision medicine, and more. The Department of Computer Science is globally recognized for its pioneering research in artificial intelligence, while the Department of Molecular Genetics is internationally acclaimed as a premier institution for biomedical and life sciences research and education. Furthermore, the Donnelly Centre for Cellular and Biomolecular Research serves as a hub for interdisciplinary collaboration, fostering the integration of functional genomics, computer science, engineering, and biology to address key challenges in biomedical research (http://www.thedonnellycentre.utoronto.ca).  Situated within the University of Toronto, one of the most concentrated biomedical research communities globally, this position provides access to extensive resources, including fully affiliated academic hospitals and research institutes. The Greater Toronto Area enhances this exceptional academic environment with its cultural and demographic diversity, as well as one of the highest standards of living in the world.   Salary: Competitive and commensurate with qualifications.  Application Process: Interested candidates should submit a CV, brief cover letter, and contact information for three references to gary.bader@utoronto.ca referencing SDL6 in the subject. Applications will be reviewed on a rolling basis. 

The Harvard Medical School Curriculum Fellows Program (HMS CFP) is seeking applications for a Biomedical Informatics Curriculum Fellow (CF) for the Department of Biomedical Informatics. This postdoctoral program targets early-career scientist-educators, emphasizing curriculum development, teaching, and educational programming in the biological and biomedical sciences. The Biomedical Informatics Curriculum Fellow collaborates with a diverse cohort of Curriculum Fellows, leveraging individual expertise while closely engaging with Harvard Medical School faculty and administration to craft, implement, and assess evidence-based graduate training. Fellows benefit from mentorship and career guidance, nurturing their growth as educators and facilitating success across various education-focused careers. Further details are available on our website (https://curriculumfellows.hms.harvard.edu/). The primary role of the Biomedical Informatics Curriculum Fellow will be to support the Master of Medical Sciences in Biomedical Informatics (MMSc-BMI) program housed in the Department of Biomedical Informatics at Harvard Medical School. This program includes a rigorous combination of core coursework and a year-long full-time thesis research project. As part of the team leading this master’s program, the curriculum fellow will ensure that the program’s offerings provide opportunities for students to develop skills in data science in the context of medicine and biological science to improve human health.   The CF will report directly to and receive mentorship from Dr. Aparna Nathan (Associate Director of the MMSc-BMI program) and Dr. Aimee Hollander (the Curriculum Fellows Program Director). The MMSc-BMI program will work with the successful candidate to identify a faculty mentor in their scientific area of expertise.   Start Date: The ideal start date for this Curriculum Fellow is approximately April 28, 2025, however there is flexibility to start later. This is a full-time position and the candidate will be expected to work in person on the HMS campus in Boston, MA 3-4 days per week. The CF appointment is renewable annually for a maximum of three years and is non-tenure-track. Application Deadline & Instructions: Applications received by February 1st, 2025 will receive a full review. Applicants who apply after the deadline must email cfp@hms.harvard.edu to alert the hiring team you’ve applied. To apply, CLICK HERE, or copy the application URL into your browser: https://academicpositions.harvard.edu/postings/14481  Below are the required application materials needed to apply: A cover letter that addresses your interest in and qualifications for the position. Please highlight your experience in teaching, curriculum development and biomedical informatics research in your cover letter. A curriculum vitae. A teaching statement. The teaching statement is an opportunity to describe your philosophy of teaching in the context of your own experiences. A discussion of diversity, equity and inclusion is an important component of the teaching statement. Submissions will be evaluated according to the guidelines found on our website, here, https://curriculumfellows.hms.harvard.edu/teaching-statement-guidelines The names and contact information of three professional references. If you have any questions specific to the program or fellowship, please email: cfp@hms.harvard.edu

Microbial biomarkers to improve the efficacy of GDNF treatment for Hirschsprung disease Context Hirschsprung disease is a deadly congenital malformation where the enteric nervous system (ENS) is missing from the colon. Recently, we found that a GDNF-based treatment can induce the formation of a new ENS in the colon of mouse models of Hirschsprung disease, and significantly improve gastrointestinal functions and survival of these mice. However, we have observed variability in the response to GDNF treatment among individuals, with some mice failing to respond while others respond positively. In collaboration with our project partner Neurenati, we are carrying out studies to understand why, and thereby improve the efficacy of the treatment.  

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.