Careers at the University of Massachusetts Lowell

General Summary of Position:

The successful applicant will conduct computational research on the project related to machine learning algorithm development for boosting muti-scale and multi-physics simulations of field-enhanced catalysis in an electrified system. The candidate will develop a machine learning-based algorithm for field-enhanced catalysis using methods such as graph neural networks, gaussian processes, graph convolutional networks, active learning, and group activity. The candidate will focus on quantifying the local field strengths, predicting the energetics influenced by fields, and optimizing the field-enhanced catalytic process. The candidate will help the immediate supervisor in writing scholarly articles and proposals. Salary will be competitive and based on the experience of the candidate.

This position is contingent upon funding. The initial appointment will be for one-year with a possibility of renewal based on productivity and successful performance and the availability of funding.

Minimum Qualifications (Required):

• Education:  PhD in Nuclear Engineering, Chemical Engineering, Materials Science, Computer Science, Chemistry, Physics, or related field.

• Skills:

  • Molecular dynamics simulation
  • Electronic structure methods (DFT)
  • Microkinetic modeling
  • Machine Learning
  • High-performance computing

Additional Considerations:

• Ability to work independently, with limited direction, as well as within a team environment.
• Ability to mentor and inspire students with limited research experience.
• Experience in heterogeneous catalysis using multi-scale simulation, group activity, and machine learning.

Special Instructions to Applicants:

Only current UML Employees within the Grants & Contracts (MTA/GRACE) bargaining unit will be considered during the first 10 business days of the posting. All other candidates will be considered after that period.

Review of applications will begin immediately and continue until the position is filled; however, the posting may close when an adequate number of qualified applications is received.

This position is contingent upon funding. The initial appointment will be for one-year with a possibility of renewal based on productivity and successful performance and the availability of funding.

Please include a resume, cover letter and research statement with your application.  Names and contact information of three references will be required during the application process.