Postdoctoral Researcher

2 months ago

Toronto, Ontario, Canada University Health Network Full time
Company Description

The University Health Network, where "above all else the needs of patients come first", encompasses Toronto 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.

Job Description

Union: Non-Union

Site: Princess Margaret Cancer Centre

Department: Princess Margaret Research Institute

Reports to: Principal Investigator

Hours: 37.5 hours per week

Salary: $51,750 minimum, annually: To commensurate with experience and consistent with UHN compensation policy

Status: Temporary Full-time (2 years)

Position Summary

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 and Schapira 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; approved or experimental drugs.

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.

Duties

  • 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 the AIRCHECK
  • Contribute to the design and execution of experiments to validate target predictions.
  • 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.
  • Strong background in AI, deep learning, and machine learning.
  • 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).
  • Strong data engineering skills.
  • Strong expertise in programming and machine learning (eg, PyTorch, VertexAI, Flask, TensorFlow).
  • Excellent communication skills and ability to work effectively in a collaborative research environment.

Preferred qualifications

  • Prior experience in computational chemistry or drug discovery research.
  • Knowledge of cheminformatics, structural bioinformatics, or systems biology.
  • Familiarity with deep learning frameworks such as PyTorch or TensorFlow.
  • Experience with high-performance computing and cloud computing platforms like Google Cloud Platform.

How to apply

Submit a CV, a copy of your most relevant paper, and the names, email addresses, and phone numbers of three references to The subject line of your email should start with "POSTDOC AIRCHECK - SGC". All documents should be provided in PDF.

Deadline

Applications must be submitted before March 17, 2024.

Team

The candidate will work in a large multidisciplinary team within the Structural Genomics Consortium and multiple associated laboratories:

  • The Haibe-Kains Lab
  • The Schapira Lab
  • The Arrowsmith Lab

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.

Additional Information

Why join UHN?

In addition to working alongside some of the most talented and inspiring healthcare professionals in the world, UHN offers a wide range of benefits, programs and perks. It is the comprehensiveness of these offerings that makes it a differentiating factor, allowing you to find value where it matters most to you, now and throughout your career at UHN.

  • Competitive offer packages
  • Government organization and a member of the Healthcare of Ontario Pension Plan (HOOPP )
  • Close access to Transit and UHN shuttle service
  • A flexible work environment
  • Opportunities for development and promotions within a large organization
  • Additional perks (multiple corporate discounts including: travel, restaurants, parking, phone plans, auto insurance discounts, on-site gyms, etc.)

Current UHN employees must have successfully completed their probationary period, have a good employee record along with satisfactory attendance in accordance with UHN's attendance management program, to be eligible for consideration.

All applications must be submitted before the posting close date.

UHN uses email to communicate with selected candidates. Please ensure you check your email regularly.

Please be advised that a Criminal Record Check may be required of the successful candidate. Should it be determined that any information provided by a candidate be misleading, inaccurate or incorrect, UHN reserves the right to discontinue with the consideration of their application.

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.

UHN is an equal opportunity employer committed to an inclusive recruitment process and workplace. Requests for accommodation can be made at any stage of the recruitment process. Applicants need to make their requirements known.

We thank all applicants for their interest, however, only those selected for further consideration will be contacted.

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