Location: 8800 Glenlyon Pkwy Burnaby, British Columbia
Career Level: Technical Level 2 or 3
Who are we?
Svante is a rapidly growing clean energy technology company making commercial-scale carbon capture and removal a reality and enabling global industries to play offense in the fight against climate change that will accelerate the global transition to a lower-carbon economy.
The growing success of our company is owed to our commitment to our people, our emphasis on our values, and our innovative technology. Passion is put into what we do every day.
Purpose of the Role:
We are seeking a motivated and qualified Research Scientist with a background in the development and application of Machine learning (ML) techniques in material science at R&D Centre of Excellence of Svante. You will have expertise in large language models, ML-based materials property prediction, and a strong familiarity with metal-organic frameworks (MOFs) and their characterization techniques.
Required Skills and Qualifications:
- You have MSc/PhD in Materials Science, Chemistry, Chemical Engineering, or a related field with a focus on computational materials science, machine learning, or a closely related area.
- You have demonstrated expertise in machine learning techniques, particularly Bayesian optimization, and their application to materials design and discovery.
- You have strong programming skills in languages such as Python, MATLAB, or R, and experience in machine learning libraries such as scikit-learn, TensorFlow, or PyTorch.
- You have experience with MOF synthesis, characterization, and data analysis techniques, with a particular emphasis on PXRD patterns.
- You have experience in materials discovery and screening.
- You have track record of publication in peer-reviewed journals and presentation at scientific conferences.
Bonus Skills and Qualifications:
- You have familiarity with molecular features in MOFs, including ligand design, metal coordination environments, pore size, and surface chemistry.
- You have familiarity with crystal structure prediction methods using machine learning techniques, such as graph neural networks, convolutional neural networks, or other advanced machine learning algorithms.
- You have advanced skills in other computational techniques such as DFT and MD.
What Success looks like in this role:
- You will conduct research to develop and apply large language models and machine learning algorithms specifically tailored for the design, synthesis, and characterization of MOFs.
- You will collaborate closely with the sorbent development team to design and screening of new sorbents optimized for CO2 capture, leveraging computational chemistry & machine learning methodologies.
- You will apply ML-based optimization techniques (including Bayesian optimization) to optimize experimental conditions for MOF synthesis and performance.
- You will develop and implement machine learning models for predicting key material properties of MOFs, such as gas adsorption capacity, selectivity, and stability.
- You will collaborate closely with experimentalists to design and interpret experiments, validate computational predictions, and guide experimental synthesis efforts.
Equal Employment Opportunity Statement
Svante is an Equal Employment Opportunity employer. Employment decisions are based on merit and business needs. All employment decisions are made without regard to race, color, national origin, ancestry, sex, gender, gender identity or expression, sexual orientation, age, genetic information, religion, disability, medical condition, pregnancy, marital status, family status, veteran status, or any other characteristic protected by law. This Equal Employment Opportunity policy applies to all practices relating to recruitment and hiring, compensation, benefits, discipline, transfer, termination and all other terms and conditions of employment. All Svante employees share in the responsibility for assuring that, by our personal actions, the policies are effective. Accommodations will be provided as requested by candidates taking part in all aspects of the selection process.
Pay Equity Statement
Svante believes in pay equity, fairness and transparency, and so we include a salary range within the job posting. Our salary ranges are determined by role, level, and location.
The actual salary offered to the final candidate is based on the salary range shown, and will vary depending on the candidate's relative experience, qualifications, and anticipated level of performance. We assess all candidates individually and strive to offer competitive and equitable compensation packages.
Base pay is one part of the Total Rewards that recognize employees for their performance and contributions. In addition to Base Pay, Svante provides comprehensive benefits to employees such as Restricted Share Units, Extended Health and Dental Plan, Retirement Saving Plan, free access to a Fitness Centre and more!