Train, fine-tune, and rigorously evaluate AI models to achieve the highest standards of performance, accuracy, and reliability in physics-based simulations.
Requirements
- 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.
- 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.
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.
Other
- Excellent communication skills, with the ability to explain complex concepts to non-expert audiences.
- Prior experience in interdisciplinary research teams involving AI applications.
