Transition from physics to AI engineering by leveraging your exceptional mathematical foundation and computational modeling experience. Physics graduates possess rare skills that translate powerfully to AI: you understand differential equations that underpin neural network optimization, have experience with Monte Carlo methods used in modern sampling techniques, and think naturally about complex systems with many interacting variables. Your background in MATLAB or Python for simulations, numerical methods for solving intractable problems, and rigorous data analysis from experimental work provides a strong foundation. The mathematical maturity required to grasp concepts like gradient descent, backpropagation, and attention mechanisms comes naturally to someone trained in Lagrangian mechanics or quantum field theory. This path focuses on bridging the gap between physical modeling and machine learning paradigms, teaching you software engineering best practices, and guiding you through the practical aspects of building production AI systems. Your research experience, designing experiments, analyzing results, and iterating on hypotheses, directly maps to the empirical nature of modern AI development. Timeline: 5-9 months depending on your programming depth and available study time.
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build