Interested in a PhD at LPTMS ?

LPTMS Internship and PhD Proposal: Out-of-equilibrium dynamics of integrable open spin chains

Contact: Veronique Terras (

Video: Out-of-equilibrium dynamics of integrable open spin chains

Quantum spin chains constitute a rare example of quantum many body interacting systems where several important physical quantities can be exactly computed. This property (quantum integrability) together with numerous theoretical and experimental applications makes these systems a perfect testing ground for non-perturbative approaches in quantum field theory. One of the most intriguing (experimentally observable) features of quantum integrable systems including spin chains is their out-of-equilibrium behaviour. We propose to study, using integrability techniques, the out-of-equilibrium properties of open spin chains where a rapid change of a boundary magnetic field can produce a macroscopic change of the system. The goal of the internship is to learn the main methods of quantum integrability (algebraic Bethe Ansatz, separation of variables) and how these methods can be applied for the computation of scalar products and form factors in open spin chains. This first step will be necessary for the further study of the relaxation dynamics after a sudden change (quench) of a boundary magnetic field. This internship will ideally be followed by a PhD, in co-direction between V. TERRAS (LPTMS, Université Paris-Saclay) and N. KITANINE (professor, Université de Bourgogne). The location of the internship and PhD will be in LPTMS.

LPTMS / PMMH ESPCI Internship and PhD Proposal: Stress reversal by a strong nonlinearity: an elastic sheet toy model

Living cells move thanks to nanometer-size molecular motors whose forces are transmitted up to the scale of the cell by a fiber network known as the cytoskeleton. On much larger length scales, individual cells generate forces that are similarly transmitted to the tissue level through the fibrous extracellular matrix. While the biology of these processes is rather well characterized, the simple problem of force transmission through these highly nonlinear elastic media is far from trivial, and leads to a conversion of local extensile forces to contractile stresses, with crucial biological implications.
To better understand this surprising physical behavior, we will set up a model force transmission experiment where the role of the nonlinear elastic medium will be played by a thin plastic sheet floating on water. By locally exerting extensile forces at the center of the sheet by inflating a balloon, we will directly observe how the forces are rectified through the wrinkling of the sheet. The goal is to help explain why the cytoskeleton is always contractile despite containing a significant number of extensile motors, and to inspire the design of counter-intuitive materials that contract when they should extend.

Expected skills: The student will have a taste for experimental physics. He/She will set up and run a model experiment, and participate in the theoretical analysis of the measurements.

Contact: Martin Lenz, Etienne Reyssat, José Bico, Benoît Roman / @:

Internship location: barre Cassan A, campus Jussieu, 75005 Paris


LPTMS Internship and PhD Proposal: Frustrated self-assembly with multiple particle types

Self-organization is key to the function of living cells – but sometimes goes wrong! In Alzheimer’s and many other diseases, normally soluble proteins thus clump up into pathological fiber-like aggregates. While biologists typically explain this on the grounds of detailed molecular interactions, we have started proving that such fibers are actually expected from very general physical principles. We thus show that geometrical frustration builds up when mismatched objects self-assemble, and leads to non-trivial aggregate morphologies, including fibers.
While we have shown that collections of identical particles form aggregates of various dimensionalities, realistic biological examples often involve multiple proteins. We will thus investigate how collections of several types of different particles typically interact and interfere. Our study will first consist in developing multi-geometries variants of the lattice-based numerical model presented in the illustration. We will then ask whether species with different geometries tend to phase separate, or conversely whether the mutiplicity of interactions they offer eases geometrical frustration and favors co-assembly. We will also wonder how this combinatorics affects the dimensionality of the aggregates, and whether we can identify generic features of the particles that distinguish between the two scenarios. We will then conduct off-lattice simulations to assess the robustness of these scenarios. Finally, we will attempt to construct a mean-field theory describing the co-assembly of a large variety of particles (> 10 or so) thus revealing the interplay between frustration and combinatorial freedom in self-assembly.
Beyond protein aggregation, this project opens investigations into a new class of “disordered” systems where the disorder is carried by each identical particle, as opposed to sprinkled throughout the system. This will help define the much-debated notion of frustration in dilute systems. This project will be conducted in collaboration with Pierre Ronceray (Turing Center for Living Systems, Marseille), who will co-direct a possible PhD project.

Expected skills:
A taste for statistical mechanics and numerical simulations connected to analytical aspects.
PMMH at ESPCI & Sorbonne U. and/or LPTMS at U. Paris-Saclay (Orsay)
Contact: or


LPTMS Internship and PhD Proposal: Self-assembly in space and time

Video: Self-assembly in space and time - Martin Lenz - LPTMS

Recent experimental developments have made assembling machines at the nanometer scales that mimic or even attempt to surpass the functions of biological objects an increasingly reasonable goal (as recognized in 2016). Despite remarkable progress in manufacturing individual nanometer-sized objects with controlled shapes however (see an example in the illustration), assembling many of them into larger structures remains an open challenge and an active field of research.
In this project we will undertake an additional challenge, namely to self-assemble such objects not only in space, but also in time. Specifically, we will explore the design principles for DNA origami particles produced by our collaborator Seth Fraden (Brandeis University, USA) to assemble over a given sequence over time, which will allow for an actin-like treadmilling (coordinated polymerization from one end, depolymerization from the other) of a polymer-like structure under e.g., temperature cycling. Suchmechanisms could be key in controlling the motor action of prospective molecular machines.
In a second stage (e.g., during a PhD), the intern may develop simulations tools to optimize particle shapesfor self-assembly of printed particles produced at PMMH in collaboration with Julien Heuvingh and Olivia du Roure.

Expected skills:  A taste for statistical mechanics, numerical simulations and working with experimentalists.

PMMH at ESPCI & Sorbonne U. and/or LPTMS at U. Paris-Saclay (Orsay)
Contact: or


LPTMS PhD Proposal: Models and Time Series Analysis for Human Sports Performance

Responsable: Thorsten Emig + 33 (0)1 69 15 31 80

This project is directed to students with a strong background in quantitative methods from statistical physics, and ideally some knowledge of machine learning, computational physiology and statistical analysis of large data. Interest in sports performance would be useful. Expected are both analytical and computer programming

Models for human sports performances of various complexities and underlying principles have been proposed, often combining data from world record performances and bio-energetic facts of human physiology. For running, we were the first to derive an observed logarithmic scaling between world record running speeds and times from basic principles of metabolic power supply. We showed that various female and male record performances (world, national) and also personal best performances of individual runners for distances from 800m to the marathon are excellently described by our approach, with mean errors of (often much) less than 1%.

Main goal of this thesis project is the data-driven modeling of physiological and biomechanical processes in endurance sports, in particular running. The physiological and mechanical response of humans to exercise constitutes a complex system that involves many dynamical variables. Examples are the beat-to-beat intervals between heart beats, oxygen uptake, and stride frequency to name a few. These variables show inherent fluctuations that can be correlated.

Time series analysis can be used to detect these correlations which can show fractal scaling. This has been demonstrated for patients with cardiac diseases by Goldberger (see references below). Methods include detrended fluctuation analysis (DFA), multifractal DFA, EMD, multiscale entropy, and transfer entropy.

Models for complex physiological systems shall be constructed by learning from data. For example, running performance has been studied using recent advances in machine learning (see reference by Blythe and Kiraly). One aspect of this project is to apply machine learning to complex physiological data for endurance exercise and compare the so obtained results to findings from other methods.

This project potentially involves collaborations with Prof. A. Goldberger (Harvard Medical School) and Prof. E. Räsänen (TUT, Finland).

The official application can be found on the web site of Ecole Doctorale at

You can also contact me directly at or at