Jobs

Laboratory: Bioinformatics and Proteomics Laboratory (ADLab) – Prof. Arnaud Droit. The laboratory specializes in the development of advanced computational methods and artificial intelligence and statistical approaches for the analysis of massive datasets. ADLab collaborates with academic and industrial partners internationally and has high-performance computing infrastructure. Institution: Faculty of Medicine, Université Laval, Quebec City, Canada

Job Description: Prof. Arnaud Droit’s laboratory (ADLab) is recruiting a bioinformatics student to work on the analysis of proteomics data generated by latest-generation mass spectrometers. This research will be conducted as part of the fight against antibiotic resistance, which represents a major threat to public health. This issue is a consequence of the overuse of antibiotics, which promotes the emergence of resistant bacteria, making many infections difficult to treat. It is therefore crucial to develop new approaches to detect and counter these resistances. The candidate will contribute to the development of computational tools for the analysis and validation of proteomics data, as well as the application of machine learning methods for identifying diagnostic biomarkers in biological fluids (detection of bacteria, treatment resistance, etc.).

Laboratoire : Laboratoire de Bioinformatique et Protéomique (ADLab) – Pr. Arnaud Droit. Le laboratoire est spécialisé dans le développement de méthodes computationnelles avancées et d’approches statistiques pour l’analyse de données massives. ADLab collabore avec des partenaires académiques et industriels à l’échelle internationale et dispose d’infrastructures de calcul de haut niveau.

Institution : Faculté de Médecine, Université Laval, Québec, Canada

Description du poste : Le laboratoire du Pr. Arnaud Droit (ADLab) recrute un(e) doctorant(e) en informatique pour développer et appliquer des algorithmes quantiques, destinés à l’analyse de données biologiques. Le projet s’articulera autour de la conception de solutions algorithmiques innovantes, appliquées à des problématiques de bioinformatique. Le ou la candidat(e) intégrera un environnement de recherche dynamique avec accès à des infrastructures de calcul avancées et des collaborations internationales.

Laboratoire : Laboratoire de Bioinformatique et Protéomique (ADLab) – Pr. Arnaud Droit. Le laboratoire est spécialisé dans le développement de méthodes computationnelles avancées et d’approches d’intelligence artificielle et statistiques pour l’analyse de données massives. ADLab collabore avec des partenaires académiques et industriels à l’échelle internationale et dispose d’infrastructures de calcul de haut niveau.

Institution : Faculté de Médecine, Université Laval, Québec, Canada

Description du poste :
Le laboratoire du Pr. Arnaud Droit (ADLab) recrute un(e) étudiant(e) en bioinformatique pour travailler sur l’analyse de données de protéomique issues de spectromètres de masse de dernière génération. Cette recherche s’effectuera dans le cadre de la lutte contre l’antibiorésistance, qui représente une menace majeure pour la santé publique. Cette problématique est la conséquence de la surutilisation des antibiotiques qui favorise l’émergence de bactéries résistantes, rendant de nombreuses infections difficiles à soigner. Il est ainsi crucial de développer de nouvelles approches pour détecter et contrer ces résistances. Le ou la candidat(e) contribuera ainsi au développement d’outils informatiques pour l’analyse et la validation de données protéomiques ainsi qu’à l’application de méthodes d’apprentissage automatique pour l’identification de biomarqueurs diagnostiques dans des fluides biologiques (détection de bactéries, résistance aux traitements, etc.).

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: 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.