Anne-Florence Raphaëlle Bitbol

I was trained in physics, and always had a strong interest in biology. I am broadly interested in understanding biological phenomena in a quantitative way, through physical concepts and mathematical and computational tools. I investigate the relative impacts of optimization and historical contingency in biological evolution, both at the scale of protein sequences and at the scale of microbial populations.Here is my research group website.Curriculum vitae Here is my complete CV. Professional experience:Since 2026: Associate Professor (tenured), Institute of Bioengineering, School of Life Sciences, EPFL, Switzerland2020-2025: Tenure-Track Assistant Professor, Institute of Bioengineering, School of Life Sciences, EPFL, Switzerland2016-2020: CNRS Researcher (tenured), Laboratoire Jean Perrin, Sorbonne Université, France2012-2016: Postdoctoral Research Fellow, Biophysics Theory Group (PIs: Ned Wingreen, William Bialek, Curtis Callan), Princeton University, USAEducation:2009-2012: PhD in Physics, summa cum laude, Université Paris-Cité (Paris-Diderot), France, "Statistics and dynamics of complex biological membranes", advised by Jean-Baptiste Fournier2007-2009: MSc in Physics, summa cum laude, ENS, Paris, France 2006-2007: BSc in Physics, summa cum laude, ENS Lyon, France Teaching & PhD PhD Students Alexandre Didier Nicolas Littiere, Agathe Bredel, Anamay Ashwin Samant, Cecilia Fruet, Hengdong Lu Past EPFL PhD Students Nicola Dietler, Richard Marie Servajean, Damiano Sgarbossa Courses EDCB seminar series BIOENG-606 The EDCB seminar series provides EDCB students the opportunity to share their research and learn from their peers. Students can freely exchange, present data, ideas and get useful feedback on ongoing research and improve communication skills. Genomics and bioinformatics BIO-463 This course covers various data analysis approaches associated with applications of DNA sequencing technologies, from genome sequencing to quantifying gene evolution, gene expression, transcription factor binding and chromosome conformation. Lecture series on scientific machine learning PHYS-754 This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the very scientific method. Randomness and information in biological data BIO-369 Biology is becoming more and more a data science, as illustrated by the explosion of available genome sequences. This course aims to show how we can make sense of such data and harness it in order to understand biological processes in a quantitative way. Awards Reviewer Excellence award American Physical Society 2025 Early Career Scientist Prize in Biological Physics International Union of Pure and Applied Physics (IUPAP) 2023 Best teacher award Life Sciences Engineering teaching section, EPFL 2023 Michelin Young Researcher (PhD) Prize French Physical Society 2014 Louis Forest PhD Prize in the Life Sciences Chancellery of the Universities of Paris 2013