Assistant Adjunct Scientist in AI driven discovery in chemistry 

An Adjunct Assistant Scientist in AI-driven discovery in chemistry position is available for computational chemists to work on a project on the AI-driven discovery in chemistry led by Dr. Mingjie Liu in the Department of Chemistry. We have a position available in our group to work on the development of Machine Learning Interatomic Potentials (MLIPs) and their applications in computational chemistry, particularly for materials discovery and design. The ideal candidate will have a strong background in applying uncertainty quantification (UQ) and active learning (AL) methods to MLIP development, as well as experience in density functional theory (DFT) and molecular dynamics simulations. The candidate will be working on cutting-edge projects involving, but not limited to, 2D and 3D structure prediction and property exploration for materials, including hydrogen storage and other applications.

Must have PhD in chemistry, materials science, physics or related. This position will be initially awarded for one year, and, contingent upon strong performance and conduct and availability of funds, may be renewed for up to one year. 

 Masters in computational chemistry, materials science, physics, or a related field.

Ph.D. in computational chemistry, materials science, physics, or a related field.

Postdoctoral research experience in related areas is highly preferred.  

Experience with data-driven approaches in materials discovery and design.

Strong publication record in peer review journals and the areas of materials modeling, MLIP development, and computational chemistry.

Experience in working with high-performance computing environments.

Strong background in machine learning applied to materials science, particularly MLIP development.

Proficiency in UQ and AL techniques.

Expertise in data preparation, analysis, and familiarity with structure prediction algorithms.

Proficiency in using computational chemistry software such as VASP, FHI-aims, Gaussian, or related codes

Experience in performing ab initio molecular dynamics (AIMD) simulations and molecular dynamics (MD) within LAMMPS.

Excellent problem-solving skills and the ability to work both independently and within the team

For full consideration, applications must be submitted online. Click on Apply Now at the top of this posting. 

A complete application includes (1) a letter of application summarizing the applicant's qualifications, interests, and suitability for the position, (2) a complete curriculum vitae, (3) a statement on research goals, and (4) a list of at least three references. After initial review, letters of recommendation will be requested from the references for selected applicants. 

Applications will be reviewed beginning October 8, and the position will remain open until filled. Only complete applications will be reviewed at this time. Applications received after this date may be considered at the discretion of the committee and/or hiring authority. 

All candidates for employment are subject to a pre-employment screening which includes a review of criminal records, reference checks, and verification of education. 

The selected candidate will be required to provide an official transcript to the hiring department upon hire. A transcript will not be considered “official” if a designation of “Issued to Student” is visible. Degrees earned from an educational institution outside of the United States require evaluation by a professional credentialing service provider approved by the National Association of Credential Evaluation Services (NACES), which can be found at http://www.naces.org/. 

The Department of Chemistry is committed to promoting an environment that welcomes all abilities, classes, ethnicities/races, gender identities and expressions. We particularly welcome applicants who can contribute to such an environment through their scholarship, teaching, mentoring, and professional service.