Date Posted: 08/10/2024
Req ID:39235
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 Research 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 Research Scientist joint with SDL3 (Medicinal Chemistry) and SDL6 (Human Organ Mimicry).

This is a 2 years term with possibility of renewal.

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 interaction with protein targets
• Representation learning related protein-ligand featurization
• De novo generation of synthesizable small molecules based on a library of available chemical building blocks (i.e., generative modeling)
• Machine learning techniques for compound property prediction

The Staff Research Scientist will work with a diverse team of leading experts at U of T, including Professors Alán Aspuru-Guzik, Anatole von Lilienfeld, Florian Shkurti, Animesh Garg, Oleksandr Voznyy, Robert Batey, Cheryl Arrowsmith, Milica Radisic, and Vuk Stambolic. The Staff Research Scientist will also work with Staff Scientists in Medicinal Chemistry, Human Organ Mimicry and AI SDL.

The Staff Research Scientists involved in the AC are highly skilled and experienced researchers who will work independently to develop the AI and automation technologies required to build robust and scalable self-driving labs, manage these SDLs, and design and implement research programs (based on the direction of the AC’s scientific leadership team) that leverage the SDL platforms to discover materials and molecules. Moreover, the Staff Research Scientists will work collectively, sharing knowledge among each other, faculty, and trainees. This role will report to the Academic Director and Executive Director of the Acceleration Consortium.

The components and duties of the work can include:

Machine learning for SDL Development

Working with the AC community, including faculty and partners, this candidate will determine the required capabilities of the SDLs to be built. Developing the plans for machine learning within the SDLs that will meet user requirements for automated material synthesis and characterization. Developing customized hardware and Python software packages to build SDLs.

The components and duties of the work can include:

1. SDL and Automation Development

Working with the AC community, including faculty and partners, to determine the required capabilities of the SDLs to be built.  Developing SDL plans to meet user requirements and designing novel instruments for automated material synthesis and characterization.  Developing customized hardware and Python software packages to build SDLs.  Selecting, procurement, and installation of the equipment required for SDLs.

1. Research Direction

Working independently to develop research programs that leverage the AC’s SDLs and supports the research objectives of AC faculty and industry partners.  Using SDLs to synthesize and characterize large quantities of candidate molecules, calibrating theoretical models with experimental data, predicting promising candidates with computational tools and machine learning algorithms, and elucidating structure-property relationships of emerging molecules, polymers, solid-state materials, formulations, etc.

Tasks include:  

• • Managing the research and development projects of AC’s industry partners when implemented in AC labs.
  • Developing plans supporting research collaborations and estimating financial resources required for programs and/or projects.
  • Working with Product Managers to ensure research outcomes meet partner requirements.
  • Promoting AC’s research capacity, including delivering presentations at conferences.
  • Collaboration in preparing and submitting research proposals to granting agencies and progress reporting. 
  • Preparing manuscripts for submission to peer review publications/journals and stewarding them through the process.

1. Other
   • Supporting consulting services related to the application of SDLs for materials discovery for the AC’s partners.
   • Support research-focused events such as Annual Symposium

MINIMUM QUALIFICATIONS:

Education – Ph.D. in chemistry, materials science, life sciences, physics, engineering, computer science, or related discipline

Experience

• One (1) to 5 years of experience (inclusive of PhD and/or post-graduate work) in accelerated research and development in the area of machine learning applications in computational chemistry and/or biology
• Experience working closely with a Principal Investigator or as a Principal Investigator or as Project Director with responsibilities of managing, developing and executing a major research project in the area of AI, machine learning, and/or advanced computational analysis and modeling of physical and/or biological phenomena.
• Experience with overseeing the activities of a lab.
• Experience working with industry partners and on industry led research and development projects.
• Strong experience presenting research at academic conferences.
• Demonstrated record of academic and/or research excellence
• Must have a strong scholarly publication record.

Skills

• Skills in electronic/hardware-oriented programming and machine learning
• Strong and effective communicator in oral and written English
• Collegial in working with team members and collaborators.
• Ability to work independently.

Other  

• Must have a strong publication record.
• Demonstrated success in writing and preparing manuscripts, presentations, reports, briefs, and scientific abstracts and manuscripts for peer-reviewed journals.

All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority

Closing Date: 12/31/2024, 11:59PM ET
Employee Group: Research Associate
Appointment Type: Grant - Term (2 years term with possibility of renewal)
Schedule: Full-Time
Pay Scale Group & Hiring Zone: R01 -- Research Associates (Limited Term): $52,574 - $98,576 (salary will be assessed and may go above the range based on skills and experience)
Job Category: Research Administration & Teaching