Postdoctoral Fellowships at the University of Toronto:

Postdoctoral Fellowship opportunities in the Sargent Group at the University of Toronto


  1. Machine Learning for the discovery of new functional materials(application to be sent to People with the following skills are encouraged to apply:

     – Experience with two or more of the following ML techniques: classical and deep learning, image recognition, reinforcement learning, NLP, RNN

     – 2+ years of experience in programming (Python preferred)

     – Version control (Gitlab/Github)

     – Experience with Density Functional Theory calculations and Linux HPC

     – Strong written and oral communication, and time management skills

     – Strong interest in applying ML to materials science problems


  1. Design of electro-optical modulators(application to be sent to People with the following skills are encouraged to apply:

     – Experience in single crystal growth

     – Experience in optics and polarization, nonlinear optics and measurements, theories of nonlinear phenomena

     – Familiarity with laboratory optical bench optics, electronics, optoelectronics, and safe use of lasers; able to design and carry out laser-based optical experiments

     – Expertise in integrated photonics, nonlinear optics, ultrafast optics, solid state physics, etc

     – Background in computational electromagnetics (FDTD, Finite Element, etc)


  1. Synthesis of III-V, II-VI, and other types of quantum dots(application to be sent to People with the following skills are encouraged to apply:

     – Hot-injection synthesis of quantum dots and the growth of shells

     – Experience in characterization of QDs

     – Experience in fabrication of QD-based devices


  1. Electrocatalytic devices(application to be sent to and People with the following skills are encouraged to apply:

     – Device experience in fuel cell, electrolyzer or high-temperature fuel cells

     – Experience in membrane technology for electrolysis

     – Experience in operando mass spectrometry system for electrochemical processes

     – Strong understanding and experience in synchrotron techniques and any in situ characterization techniques

     – Strong understanding and experience in all electroanalytical techniques


  1. Hybrid-nanomaterials for optoelectronic applications(application to be sent to and People with the following skills are encouraged to apply:

     – Experience in organic, quantum dot and/or perovskite materials;

     – Experience in interface characterization and engineering;

     – Device experience in solar cell, sensors and/or LEDs;

     – Strong background in charge transport and energy transfer, ideally combined with modeling capabilities;

     – Strong background in device characterization


Our group unites chemistry, physics, and engineering within eight experimental labs at the University of Toronto. Our mission is to advance the physics and chemistry of optoelectronic materials and devices, and to apply this knowledge to address key challenges in sustainable energy. Our group fosters win-win teamwork culture for new scientific avenues and excellence.

We are recruiting multiple postdoctoral fellows in the areas mentioned above. Candidates with demonstrated expertise in these areas are encouraged to apply. The research is aimed at building functional devices that appreciably exceed existing performance records. The principal goal will be first-authored publications in high-impact journals; accompanied with an expectation that the candidate will devote a portion of time to mentoring graduate students in the group, developing new research directions, and attracting external support for the research mission of the group.

Applications (including a summary of proposed research) should be sent to the emails provided above. Please specify in the subject the area(s) you applying for, and list 3 referees who have confirmed that they are willing to supply letters of reference upon request.

MASc and Ph.D. degree positions

We are recruiting graduate students for M.A.Sc. and Ph.D. degree positions. Please email ( with your CV and unofficial transcripts to initiate a conversation about opportunities in the Sargent group. Please go to for information on applying for graduate studies in ECE at the University of Toronto.


Computational discovery of new energy materials: A collaboration between the University of Toronto, the National Research Council of Canada, and Carnegie Mellon University

Are you looking to work at the forefront of energy materials discovery? Successful candidates will have the opportunity to work with two world-leading theorists and an experimentalist at three outstanding institutions. They will solve real-world problems and simultaneously deepen their computational expertise. We are looking for driven, innovative researchers to work on projects that lever leadership in deep learning and outstanding supercomputer resources to discover new materials for renewable energy (storage and generation).

Position details:

– The candidates will work collaboratively among groups at the NRC, the University of Toronto, and Carnegie Mellon University. This is an opportunity to combine expertise, methods, and datasets.

– Based on expertise, positions available, and geographic considerations, the candidates will hold a full-time appointment at one of the three institutions (NRC, UT, CMU), and will engage regularly online and in-person in addition with the collaborating tri-institution team.

Ideally, candidates will have:

– Expertise in computational chemistry and physics – such as:

o The application of quantum chemistry methods to investigate reaction mechanisms and structure-property relations in electrocatalysis or thermocatalysis;

o And/or the application of quantum chemistry and band structure methods to calculate crystal and electronic structures of semiconductor materials;

– Expertise in high-throughput DFT: familiarity with developing, running, and analyzing calculations to explore wide chemical spaces and have significant experience in python and working in a linux development environment;

– Strong written and oral communication skills, time management skills, and desire to work in research teams.

Candidates may also bring, and/or may acquire during their period as researchers on the present projects:

– Experience with code sharing and documentation for collaboration projects.

– Familiarity with machine learning techniques such as traditional feature-based machine learning, graph convolution methods, deep learning, image recognition, generative models, reinforcement learning, NLP. Comfort with machine learning frameworks such as pytorch, tensorflow, keras, etc.

– Skill in collaborating with experimental teams and applying insights obtained using ML + DFT to the discovery and validation of new catalysts and new semiconductors.

Please apply with a cover letter, your CV, and the names of three referees. Application packages should be emailed to each of:

Ted Sargent-

Isaac Tamblyn –

Andrew Johnston –

Zachary Ulissi –

The hiring committee will begin reviewing applications on September 15, 2019.

The team also encourages applications from prospective doctoral students through regular graduate admissions channels.


The University of Toronto is strongly committed to diversity within its community and especially welcomes applications from visible minority group members, women, Aboriginal persons, persons with disabilities, members of sexual minority groups, and others who may contribute to the further diversification of ideas.