Postdoctoral Fellowship in AI Safety and Mechanistic Interpretability

This postdoctoral fellowship in AI Safety and Mechanistic Interpretability requires advanced expertise in artificial intelligence, with a focus on understanding and improving the safety and reliability of AI systems. Candidates should possess a strong background in machine learning, deep learning, and neural network architectures, enabling them to analyze and interpret complex AI models at a mechanistic level. Proficiency in programming languages commonly used in AI research, such as Python, and experience with AI frameworks like TensorFlow or PyTorch, are essential. The role demands skills in designing experiments, developing novel interpretability techniques, and applying rigorous scientific methods to uncover the inner workings of AI systems. A solid foundation in mathematics, statistics, and theoretical computer science is important to support mechanistic analysis and safety evaluations. The fellowship is suited for individuals with a doctoral degree in computer science, AI, or related fields, emphasizing research excellence and innovation in AI safety. Strong analytical thinking, problem-solving abilities, and the capacity to communicate complex technical concepts clearly are critical. Collaborative skills are also valuable, as the role may involve working with interdisciplinary teams focused on advancing AI safety research. Overall, this position is ideal for candidates committed to advancing the understanding of AI mechanisms and ensuring the development of safe and interpretable AI technologies.