Description

Job Description

Required Applicant Materials:

Interested candidates should submit the following materials:

• Curriculum Vitae
• Contact information for at least three professional references

For further inquiries about this position, please email Dr. Bondar, at [email protected]

Responsibilities

The appointee will develop:

• AI surrogates for many-body spectra and transport dynamics,
• KAN-based and deep learning models for scientific discovery in quantum systems
• reinforcement learning methods for inverse design of barrier potentials and control protocols,
• generative models for discovering new asymmetric barrier geometries,
• anomaly-detection and interpretable AI tools for identifying hidden resonances and new transport regimes.

Qualifications

Applicants must have:

• a Ph.D. in Physics, AI, Computer Science, Applied Mathematics, Electrical Engineering, or a closely related field;
• strong expertise in machine learning, deep learning, and scientific computing;
• demonstrated proficiency in Python and PyTorch and/or TensorFlow;
• research experience in physics-informed AI, generative AI, reinforcement learning, quantum machine learning, or many-body quantum dynamics;
• a strong record of interdisciplinary research and publication.

About Us

Tulane University is an equal opportunity educator and employer committed to providing an education and employment environment free of unlawful discrimination, harassment, and retaliation. Legally protected demographic classifications (such as a person's race, color, religion, age, sex, national origin, shared ancestry, disability, genetics, veteran status, or any other characteristic protected by federal, state, or local laws) are not relied upon as an eligibility, selection or participation criteria for Tulane's employment or educational programs or activities.

Tulane University is responsible for providing reasonable accommodations to individuals with disabilities throughout the applicant screening process. If you need assistance in completing an application or during any phase of the interview process, please contact the Office of Human Resources by phone at 504-865-4748 or by email at [email protected].