Date Posted: 08/09/2024
Req ID: 39236
Faculty/Division: Faculty of Arts & Science
Department: Acceleration Consortium
Campus: St. George (Downtown Toronto)
Description:
Description:
The Acceleration Consortium (AC) at the University of Toronto (U of T) is leading a transformative shift in scientific discovery that will accelerate technology development and commercialization. The AC is a global community of academia, industry, and government that leverages the power of artificial intelligence (AI), robotics, materials sciences, and high-throughput chemistry to create self-driving laboratories (SDLs), also called materials acceleration platforms (MAPs). These autonomous labs rapidly design materials and molecules needed for a sustainable, healthy, and resilient future, with applications ranging from renewable energy and consumer electronics to drugs. AC Staff Research Scientists will advance the field of AI-driven autonomous discovery and develop the materials and molecules required to address society’s largest challenges, such as climate change, water pollution, and future pandemics.
The Acceleration Consortium (AC) promotes an inclusive research environment and supports the EDI priorities of the unit.
Hiring is occurring on a rolling intake. Please apply ASAP and do not wait for the listed job closing date.
The Acceleration Consortium received a $200M Canadian First Research Excellence Grant for seven years to develop self-driving labs for chemistry and materials, the largest ever grant to a Canadian University. This grant will provide the Acceleration Consortium with seven years of funding to execute its vision.
The AC is developing seven advanced SDLs plus an AI and Automation lab:
• SDL1 - Inorganic solid-state compounds for advanced materials and energy
• SDL2 - Organic small molecules for sustainability and health
• SDL3 - Medicinal chemistry for improving small molecule drug candidates
• SDL4 - Polymers for materials science and biological applications
• SDL5 - Formulations for pharmaceuticals, consumer products, and coatings
• SDL6 - Biocompatibility with organoids / organ-on-a-chip
• SDL7 - Synthetic scale-up of materials and molecules (University of British Colombia partner lab)
• A central AI and Automation lab to support all the SDLs
Position Overview:
We are seeking a motivated and skilled Staff Scientist to join the Acceleration Consortium working with the Medicinal Chemistry and Human Organ Mimicry SDLs. The ideal candidate should have strong expertise performing machine learning (ML), computational chemistry and/or computational biology with the capability and/or experience to apply those skills toward imaging data (e.g., microscopy) and/or modeling biomolecular interactions (e.g., virtual screening, docking). The candidate must have knowledge of current machine learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization for molecular design is preferred. As such, the candidate is expected to have strong coding skills in Python or another suitable language to accomplish these tasks and a correspondingly suitable publication or qualification record. This individual will play a pivotal role in supporting SDL research projects including the Human Organ Mimicry and Medicinal Chemistry and will collaborate closely with our interdisciplinary team of scientists and engineers. Additional expertise related to chemical and biological knowledge of the experimentation required to gather imaging and binding affinity data is an optional benefit.
This posted position is for a Staff Scientist joint with SDL3 (Medicinal Chemistry) and SDL6 (Human Organ Mimicry).
Expertise that is desired:
Computational expertise
• Life science and physical science applications of machine learning in chemistry and/or biology
• Programming and high-performance computing
• Experience with programming languages and scripting methods (Python, MATLAB, C++, CUDA, Bash, and/or SQL) and machine learning / deep learning methods.
• Active learning, exploration, optimal experiment design, Bayesian optimization, reinforcement learning, and/or representation learning
• Experience with chemoinformatics (clustering, SAR analysis, molecular fingerprints) and familiarity with chemical database software
Additional expertise that is desired (but not required):
• Experience with ML-based tools for image-analysis and signal processing, development of ML prediction tools
• Experience with PyTorch and/or TensorFlow, experience with databases and high-content imaging platforms
• Experience with molecular docking by physics-based (e.g., AutoDock Vina, GLIDE) and/or machine learning-based (e.g., DiffDock) of small molecule ligands to protein targets with a given crystal structure for go-no-go screening of ligands for experimentation
• Experience with free-energy perturbation (FEP)
• Virtual screening of small molecules for their potential for binding int
Location: Toronto, ON, Canada
Posted: Sept. 19, 2024, 11:26 p.m.
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