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    Postdoctoral Researcher - Toronto, ON, Canada - University Health Network

    University Health Network
    University Health Network Toronto, ON, Canada

    1 week ago

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    Description

    The University Health Network, where "above all else the needs of patients come first", encompassesToronto General Hospital, Toronto Western Hospital, Princess Margaret Cancer Centre, Toronto Rehabilitation Institute and the Michener Institute of Education.

    The breadth of research, the complexity of the cases treated, and the magnitude of its educational enterprise has made UHN a national and international resource for patient care, research and education.

    With a long tradition of ground breaking firsts and a purpose of "Transforming lives and communities through excellence in care, discovery and learning", the University Health Network (UHN), Canada's largest research teaching hospital, brings together over 16,000 employees, more than 1,200 physicians, 8,000+ students, and many volunteers.

    UHN is a caring, creative place where amazing people are amazing the world.

    Site:
    Princess Margaret Cancer Centre


    Department:
    Princess MargaretResearch Institute

    To commensurate with experience and consistent with UHN compensation policy

    We seek a postdoctoral fellow to develop innovative machine learning (ML) and deep learning (DL) methods to predict drug-like small molecules from large chemical screens based on the DNA Encoded Library (DEL) and Affinity Selection Mass Spectrometry (ASMS) technologies.

    The candidate will be hosted by the Structural Genomics Consortium (SGC ),Haibe-Kains andSchapira labs.

    The candidate will be responsible for aggregating the existing and future drug screening data and developing novel integrative machine learning/AI models for the identification of potential drug targets; The candidate will be assisted by software developers to leverage internal tools (AIRCHECK ) in order to develop, train and test ML and DL predictive models, independently or in collaboration with other experts in the Haibe-Kains and Schapira labs and other SGC collaborators.

    Develop and implement novel ML/DL algorithms to predict drug-like small molecules from large-scale chemical screening datasets.
    Utilize computational approaches to analyze complex drug screening data and extract meaningful insights.
    Collaborate with computational biologists and software developers to build, validate predictions and deploy models on theAIRCHECK
    Stay abreast of the latest advancements in machine learning, AI, and chemical biology research.
    Qualifications Doctorate in computational chemistry, computer science, engineering, applied physics or equivalent.
    Published/submitted papers in Scientific Journal or Conference Proceedings.
    Experience with analysis of high-throughput drug screening data, such as DNA-Encoded Library (DEL) and/or Affinity Selection Mass Spectrometry (ASMS).
    Excellent communication skills and ability to work effectively in a collaborative research environment.
    Prior experience in computational chemistry or drug discovery research.
    Knowledge of cheminformatics, structural bioinformatics, or systems biology.
    Experience with high-performance computing and cloud computing platforms like Google Cloud Platform.

    Submit a CV, a copy of your most relevant paper, and the names, email addresses, and phone numbers of three references tosoleil.

    The subject line of your email should start with "POSTDOC AIRCHECK - SGC".

    Founded in 2003, the Structural Genomics Consortium (SGC) is a pre-competitive public-private partnership in the areas of structural and chemical biology dedicated to open science and drug discovery.

    SGC collaborates with researchers worldwide to support breakthrough research in protein-focused open science tools, knowledge, and reagents.

    As part of their ongoing projects, they employ advanced methodologies such as high-throughput protein production and assays, and structure-guided development of chemical probes.

    SGC is the largest contributor of human protein structures to the Protein Data Bank, which enabled Google DeepMind's breakthrough in protein structure prediction.

    SGC is now embarking on a mission to be the largest contributor of chemical screening data to enable an AI-driven breakthrough in computational drug design, with AIRCHECK as the core repository.

    Government organization and a member of the Healthcare of Ontario Pension Plan (HOOPP )
    A flexible work environment
    Additional perks (multiple corporate discounts including: travel, restaurants, parking, phone plans, auto insurance discounts, on-site gyms, etc.)

    All UHN Employees are required to be fully vaccinated with a COVID-19 vaccine series, approved by Health Canada or the World Health Organization, as a condition of hire.

    Proof of COVID-19 vaccination will be required.

    Should you be the successful candidate, you will be required to comply with UHN's mandatory Vaccination Policy that is in effect.

    Requests for accommodation can be made at any stage of the recruitment process.
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