Physics PhD - AI Trainer

Job Title: Physics PhD - AI Trainer

Job Type: Part-time

Location: Remote

Job Summary:

We are seeking a highly skilled Physicist to join our team. In this role, you will leverage your deep expertise in physics, advanced mathematics, and analytical reasoning to train, fine-tune, and rigorously evaluate AI models. Your background in scientific methodology and critical thinking will be essential in ensuring our AI systems achieve the highest standards of performance, accuracy, and reliability.

Key Responsibilities:

• Design sophisticated evaluation frameworks that challenge AI systems in simulations of complex physical environments, focusing on adaptive learning, physical realism, and system response to real-world variables.
• Research, define, and validate optimal AI behaviors in physical modeling by analyzing experimental data, computational simulations, peer-reviewed research, and domain-specific case studies.
• Conduct in-depth, iterative testing of AI components such as physics-based simulations, predictive modeling engines, and adaptive systems, identifying inaccuracies, points of failure, and opportunities for enhanced fidelity.
• Develop robust scoring rubrics and evaluation matrices to consistently assess AI performance across scientific accuracy, predictive reliability, adaptability, and alignment with established physical principles.
• Document and report findings through comprehensive feedback cycles, providing actionable insights to refine AI models and guide future development in physics-driven AI systems.

Required Skills and Qualifications:

• Ph.D. in Physics or a closely related field (e.g., Applied Physics, Astrophysics, Mathematical Physics, Engineering Physics).
• Solid foundation in scientific research methods, statistical analysis, and data interpretation.
• Strong critical thinking and problem-solving skills, particularly with complex, multi-variable systems.
• Experience with programming languages such as Python, MATLAB, or Julia, particularly for data analysis or modeling.
• Familiarity with machine learning concepts (e.g., supervised learning, unsupervised learning, reinforcement learning) is highly preferred.
• Excellent communication skills, with the ability to explain complex concepts to non-expert audiences.

Preferred Qualifications:

• Hands-on experience with AI/ML frameworks (e.g., TensorFlow, PyTorch).
• Experience working with large datasets and knowledge of data preprocessing techniques.
• Background in computational physics or numerical simulation.
• Prior experience in interdisciplinary research teams involving AI applications.