Anne-Florence Bitbol

photo placeholder image

anne-florence.bitbol@epfl.ch +41 21 693 23 25 https://www.epfl.ch/labs/bitbol-lab/

Here is my CV
Here is my research group website

EPFL SV IBI-SV UPBITBOL
AAB 1 44 (Bâtiment AAB)
Station 15
1015 Lausanne

+41 21 693 23 25
Office:  AAB 1 44
EPFL > SV > IBI-SV > UPBITBOL

EPFL > SV > SV-SSV > SSV-ENS

Web site:  Web site:  https://sv.epfl.ch/education

vCard
Administrative data


Professional course

Tenure-Track Assistant Professor

Institute of Bioengineering, School of Life Sciences

EPFL, Switzerland

Since 2020

CNRS Researcher (tenured)

Laboratoire Jean Perrin

Sorbonne Université, France

2016-2020

Postdoctoral Research Fellow

Biophysics Theory Group (PIs: Ned Wingreen, William Bialek, Curtis Callan)

Princeton University, USA

2012-2016


Education

PhD in Physics

Statistics and dynamics of complex biological membranes, advised by Jean-Baptiste Fournier, summa cum laude

Université Paris-Cité (Paris-Diderot), France

2012

MSc in Physics

Fundamental physics, summa cum laude

ENS, Paris, France

2009

BSc in Physics

Summa cum laude

ENS Lyon, France

2007


Awards

Best teacher award

Life Sciences Engineering teaching section, EPFL

2023

Young Scientist Prize in Biological Physics

International Union of Pure and Applied Physics (IUPAP)

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

Teaching & PhD

Teaching

Life Sciences Engineering

PhD Programs

PhD Students

Fruet Cecilia, Littiere Alexandre Didier Nicolas, Sgarbossa Damiano,

Past EPFL PhD Students

Dietler Nicola , Servajean Richard Marie ,

Courses

Randomness and information in biological data

Genomics and bioinformatics

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.

EDCB seminar series

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.

Lecture series on scientific machine learning

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.