Animals explore their environments through embodied interactions, creating intrinsically correlated sequences of experiences. These correlations reflect the constraints imposed by their bodies, senses, and the continuous nature of space and time. In neuroscience, understanding how biological systems navigate and learn from such correlated trajectories is key to unraveling mechanisms of motor control and decision-making. Similarly, in robotics and machine learning, correlated experiences pose challenges for efficient exploration and learning, as traditional reinforcement learning frameworks often rely on independent and identically distributed data. This interdisciplinary internship will focus on developing and analyzing non-reversible stochastic processes as a framework for efficient exploration and learning in high-dimensional action spaces. This work will integrate numerical simulations of embodied systems with analytical approaches to study quantitative aspects of these processes, such as convergence rates and exploration efficiency. The intern will join an interdisciplinary research collaboration bridging computational neuroscience and mathematics. Supervised by Alexander Mathis (EPFL, Switzerland) and Manon Michel (UCA, France), the internship can be primarily based at either EPFL or UCA.