Research Associate in Theoretical and Computational Condensed Matter Physics

ORGANIZATION OVERVIEW

The Condensed Matter Physics and Materials Science Division (CMPMSD) at Brookhaven National Laboratory conducts a wide-ranging research program investigating novel and quantum materials, as well as their potential applications. Within CMPMSD, the Condensed Matter Theory group develops and applies complementary theoretical and computational approaches to study various aspects of correlated and topological materials. We also work in close collaboration with experimental groups specializing in transport measurements, and advanced scattering techniques using neutrons and x-rays.

POSITION DESCRIPTION

We invite applications for a postdoctoral research associate position in theoretical and computational condensed matter physics. The successful candidate will conduct research on correlated and topological systems using state-of-the-art machine learning and electronic structure techniques. The primary focus of the project is to design and implement machine learning pipelines to efficiently investigate electron and spin systems. The position offers close collaboration with both theoretical and experimental groups within CMPMSD.

This appointment is for two years, with the possibility of renewal for a third year depending on funding availability and research performance.

Essential Duties and Responsibilities:

• Perform research on correlated electron and spin systems using ab-initio, model Hamiltonian and machine learning-based methods.
• Publish high-quality papers and give external presentations on your work.
• Collaborate actively with various theoretical and experimental groups within CMPMSD.

POSITION REQUIREMENTS

Required Knowledge, Skills, and Abilities:

• Ph.D. in Theoretical Condensed Matter Physics, Chemistry, Materials Science, or related discipline.
• Demonstrated expertise in machine learning techniques and proficiency in scientific programming (Python and/or Fortran/C++).
• Strong publication record in peer-reviewed journals and presentations at professional conferences, or equivalent public research contributions (e.g., GitHub repositories).

Preferred Knowledge, Skills, and Abilities:

• Knowledge of Density Functional Theory calculations and Wannier method-based spin Hamiltonian downfolding techniques.
• Familiarity with high throughput computing using modern GPU hardware as well as other computational methods such as Monte-Carlo and mean-field calculations.

Brookhaven Laboratory is committed to providing fair, equitable and competitive compensation. The full salary range for this position is $75,000 - $83,000/ year. You will be placed at the level and salary commensurate with your experience.  Salary offers will be commensurate with the final candidate’s qualification, education and experience and considered with the internal peer group.

BNL policy requires that after obtaining their PhD, eligible candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience as a post-doc and/or in an R&D position, excluding time associated with family planning, military service, illness or other life-changing events.

At Brookhaven National Laboratory we believe that a comprehensive employee benefits program is an important and meaningful part of the compensation employees receive. Our benefits program includes, but is not limited to:

• Medical, Dental, and Vision Care Plans
• Flexible Spending Accounts
• Paid Time-off and Leave Programs (vacation, holidays, sick leave, paid parental leave)
• 401(k) Plan
• Flexible Work Arrangements
• Tuition Assistance, Training and Professional Development Programs
• Employee Fitness/Wellness & Recreation:  Gym/Basketball Courts, Weight Room, Fitness Classes, Indoor Pool, Tennis Courts, Sports Clubs/Activities (Basketball, Ping Pong, Softball, Tennis)

Brookhaven National Laboratory and the Energy and Photon Sciences Directorate are committed to your success. We offer a supportive work environment and the resources necessary for you to succeed.