Staff Research Scientist - Toronto, Canada - University of Toronto

University of Toronto

Verified Company

Toronto, Canada

1 week ago

Posted by:

Sophia Lee

beBee Recruiter

Description

Date Posted:11/20/2023

Req ID: 35046

Faculty/Division:
Faculty of Arts & Science

Department:
Acceleration Consortium

Campus:
St. George (Downtown Toronto)

Description:

The Acceleration Consortium received a 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

These include:

Inorganic solidstate materials,
Organic small molecules for advanced materials,
Drug discovery with chemical probes,
Formulations for pharmaceuticals, consumer products, and coatings,
Biocompatibility (organonachip), and
Synthetic scaleup of materials and molecules.

This posted position is for a Staff Research Scientist within SDL1: Inorganic

Expertise in one or more of the following areas is desired:

Inorganic materials expertise

Highthroughput inorganic materials synthesis and processing
Structural, electrochemical, and/or (opto) electrical characterization of inorganic materials
Physical, theoretical and/or computational inorganic materials chemistry and physics
Applied inorganic materials expertise in one or more of: energy storage and conversion, electrochemical systems, catalysis, optoelectronics, corrosionresistance, high temperature structural stability, and more.

Artificial intelligence / automation expertise

Machine learning in inorganic materials science
Robotics and automation
Experimental planning and design/optimization
Programming and highperformance computing
Materials informatics

The Staff Research Scientists will work with a diverse team of leading experts at U of T, including Professors David Sinton, Jason Hattrick-Simpers, Yu Zou, and more.

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:

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 the plans for SDLs that will 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.

2. Research Direction

Tasks include:

Implementing research and development projects of AC's industry partners when implemented in AC labs.
Developing plans that support research collaborations and planning 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 the preparation and submission of research proposals to granting agencies and progress reporting.
Preparing manuscripts for submission to peerreviewed publications/journals and stewarding them through the process.

3. Other

Support researchfocused events such as the Annual Symposium

MINIMUM QUALIFICATIONS:

Education - Ph.
D. in Physics.

Experience

1 to 5 years of experience in combining machine learning and highthroughput first principles calculations to accelerate discovery.
Experience working with industry partners and on industry lead research and development projects.
Expert knowledge of AI and automation and experience with the development of selfdriving laboratories.
Experience presenting research at academic conferences.
Experienced in AI for materials science: python packages, treebased models & graph neural networks, active learning, model interpretability & dimension reduction techniques.
Experienced user of High-Performance Computing (HPC) Linux clusters.

Skills