This is for a PhD opportunity starting October 2025. Context: This PhD is part of a project that aims to uncover how energy-driven processes at the molecular scale influence the organization of living matter. By focusing on how molecular activity affects cellular properties, the project will explore how these processes scale up to impact tissues. Using a combination of experiments, physical modeling, and machine learning, this research seeks to deepen our understanding of how nonequilibrium activity drives organization in biological systems. The Project: The PhD project will focus on understanding how nonequilibrium molecular activity, such as enhanced diffusion, impacts cellular properties and organization. The student will combine experimental approaches with physical modeling. This interdisciplinary approach will bridge data-driven analysis (variational autoencoders), experimental measurements (optical tweezers and microscopy), and theoretical models (stochastic thermodynamics) to elucidate the role of nonthermal fluctuations in shaping cellular behaviors. Profile: The ideal candidate should have a Masters degree in physics (or equivalent), with experience in experiments, data analysis, and programming. Knowledge of machine learning or computational modeling is a plus. A collaborative mindset and willingness to work across disciplines will be key.