Postdoctoral Scholar: ML for Physical Systems & Materials

The Friedman School of Nutrition Science and Policy; Medford (MA); Hybrid; USD 61,000 - 74,000; Full time

Job summary

The Friedman School of Nutrition Science and Policy at Tufts University invites applications for a Postdoctoral Scholar in Machine Learning for Physical Systems. The role focuses on developing ML emulators and surrogate models for chaotic dynamics and materials modeling, with opportunities to shape the research alongside the PI.

Responsibilities include independent and collaborative research, implementing and validating scientific ML models, publishing results, and contributing to grant

Qualifications

• PhD in electrical and computer engineering, physics, applied mathematics, computer science, or closely related field.
• Strong record of research and ability to work independently.
• Proficiency in scientific computing and modern ML frameworks.
• Preferred: PDEs, dynamical systems, turbulence, materials modeling, or ML surrogate models; differentiable programming.

Responsibilities

• Conduct independent and collaborative research in ML for physical systems.
• Develop and validate scientific ML models and code.
• Disseminate results through publications and presentations; contribute to grant activity.
• Mentor student researchers as part of the project.

Skills

ML frameworks

Scientific computing

Dynamical systems

PDEs experience

Turbulence modeling

Materials modeling

ML surrogate models

Differentiable programming

Research excellence

Python

Education

PhD (completed/expected)

Tools

Python

PyTorch

TensorFlow

Job description

The Friedman School of Nutrition Science and Policy at Tufts University invites applications for a Postdoctoral Scholar in Machine Learning for Physical Systems. The role focuses on developing ML emulators and surrogate models for chaotic dynamics and materials modeling, with opportunities to shape the research alongside the PI.

Responsibilities include independent and collaborative research, implementing and validating scientific ML models, publishing results, and contributing to grant