Jobs
704 days ago
University of Ottawa -
712 days ago
Laval University -
OVERVIEW Our laboratory examines the role of platelets and their mother cell, the megakaryocyte, in systemic lupus erythematosus (SLE). Megakaryocytes are giant cells, capable of replicating their DNA content 128 times without cellular division, which afford these cells an extraordinary RNA content.
PRELIMINARY OBSERVATIONS The accumulation of autoantibodies and autoantigens in blood, the formation of immune complexes (ICs) and their deposition in organs play a central role in this pathogenesis. Up-regulation of the type I interferon (IFN), leading to expression of IFN-regulated genes, also characterizes SLE. We demonstrated that dependently of the expression of FcγRIIA, a receptor for ICs, platelets displayed an altered transcriptome presenting IFN-regulated genes and dysregulated mitochondrial pathways in SLE. As platelets are anucleate, these data indicate that megakaryocytes, the cells that produce platelets and afford them their RNA content, are altered in SLE. We will integrate these intriguing findings by examining megakaryocytes in SLE with emphasis on megakaryocyte spatial transcriptomics and bioenergetics.
HYPOTHESIS: An SLE-prone environment reprograms megakaryocytes into a pro-inflammatory phenotype that plays a significant role in driving SLE pathology.
775 days ago
McGill University -
Professor Audrey Grant is hiring two PhD students at the Faculty of Medicine and Health Sciences at McGill University (the specific department depends on the background of the student). These research opportunities are focused on applied approaches directed towards chronic pain development, conditions featuring chronic pain, and pain sensitivity. 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. Although the genetic basis of various pain conditions (including fibromyalgia, migraine, lower back pain) is not well understood, the development of chronic pain implicates both the nervous system and immunity, and may be related to psychiatric outcomes. The overall goal of our joint research interests is to identify the molecular basis for the transition from acute to chronic pain, body site specificity vs. widespreadness across pain conditions, and pain sensitivity.
Moreover, pain and discomfort in everyday life are often treated with over-the-counter (OTC) analgesic medications (pain killers), particularly in Western countries, making analgesic medication intake a key exogenous exposure impacting on pain experience. We also aim to use pharmacogenomics approaches to consider the role of genetic variation in analgesic efficacy.
Methodology employed includes classical and emerging statistical genomics analysis techniques including machine learning. We offer a stimulating research environment at the Alan Edwards Center for Pain Research, an internationally recognized pain research institute. We are located near the McGill Genome Center, offering opportunities for exchanges with McGill’s Genomics experts. High performance parallel computing infrastructure is available through our dedicated resource allocation from the Digital Research Alliance of Canada, allowing for efficient implementation of Big Data projects.
782 days ago
The University of Western Ontario -
We have several graduate positions (PhD or MSc) available for individuals interested in single-cell molecular technologies, bioinformatics and data science. Possible research topics include:
– Identifying signatures to predict treatment response in breast cancer
– Single-cell genomics of immune responses to cancer
– Microbial single-cell transcriptomics
New students can join the lab via the Anatomy and Cell Biology or Biochemistry graduate programs and will have the opportunity to join Western’s Collaborative Specialization in Machine Learning in Health and Biomedical Sciences.
787 days ago
University of Oslo -
The PhD position is one of four PhD and post-doc positions within the UiORealArt Convergence environment at the University of Oslo. Convergence environments are interdisciplinary research groups that will aim to solve grand challenges related to health and the environment. They are funded by UiO’s interdisciplinary strategic area UiO:Life Science www.uio.no/life-science.
The aim of this Convergence environment is to improve causal inference in perinatal pharmaco-epidemiology using machine learning approaches on real-world and artificial data” (UiORealArt, 2022-2026). The project unites expertise within natural sciences and medicine (pharmacology, machine learning, bioinformatics, statistics, genetics, and epigenetics) with social/educational sciences (psychology, language development and educational attainment).
Perinatal pharmaco-epidemiology refers to a field that studies the safety of medication use during pregnancy. Causal inference in perinatal pharmaco-epidemiology is extremely challenging because of the nature of allowed data collection procedures while the methodological progress has on some aspects stagnated. There is currently a surge of interest in applying machine learning (ML) to epidemiological questions under the assumption that it would improve causal inference. There has also been an increased interest in incorporating high-dimensional molecular data together with epidemiological data from national health registries to strengthen causal inference. However, both the application of ML and the incorporation of high-dimensional molecular data appear to have been mostly based on hype and popular trends rather than a deeper appreciation for problem characteristics and suited inductive biases.
The PhD candidate will build on the combined expertise of epidemiologists and machine learners involved in RealArt and will work at the intersection of bioinformatics, machine learning and causal inference. First, while causality has traditionally been focused on linear relations involving data of limited size/dimensionality, there is currently an increasing interest in considering higher complexity (non-linear) relations and using data of larger size and higher dimensionality. Second, while machine learning has traditionally been focused on reflecting statistical associations in fixed underlying distributions – based on a dataset assumed to be an iid representation of this underlying distribution – there has lately been an increased realization that real-world application of machine learning very often involves substantial domain shifts, and that causality provides perspectives that are very useful to reason about and to improve the generalizability/robustness of ML models in face of such challenges.