Interested in a PhD at LPTMS ?

LPTMS Internship proposal: Using statistical models to unravel the gene interactions behind developmental robustness


Contact : Antoine Fruleux (

LPTMS Internship proposal: Conformal field theories for statistical models


contact: Raoul Santachiara (

LPTMS Internship proposal: The quantum Zeno effect in a dissipative SU(N) atomic gas

contact: Leonardo Mazza (

The quantum Zeno effect is one of the most remarkable quantum effects associated to open quantum system. In its standard formulation, it states that the continuous measurement of an unstable quantum system prolongs its lifetime. Moreover, a careful analysis shows that this enhanced lifetime can also be the consequence of a strong coupling to an environment, which acts as an unread measurement apparatus.
In this internship, that can become a Ph.D. thesis, we will consider what happens when a quantum simulator interacts with an environment, so strongly that the Zeno regime sets in.
We will specifically consider the case of ultra-cold gases, and more in particular the case of atomic gases with an SU(N) nuclear spin (such as fermionic ytterbium or strontium). Here dissipation can be represented by atomic losses from the gas, or simply by heating and dephasing; in all cases, it can be controlled from outside and tuned to the Zeno regime at will.
The interplay of the quantum Zeno effect with the unconventional spin symmetry of these gases is a promising way for creating in a dissipative fashion entangled states or topological states, here we want to further investigate this. Experiments on this subject are being developed allover the world [5] but also in the Parisian area, and interactions with experimentalists is possible.


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 Proposal: Self-assembly of irregular micro-particles

Contact: Martin Lenz, Olivia du Roure, Julien Heuvingh 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