Some of our PhD positions may already be funded on a specific contract, in which case the corresponding offer does provide the information. In other cases, candidates interested will have to apply for a contract through the yearly competition of the "Physique en Ile de France" doctoral school, taking place in April-May. More details are available here : https://www.edpif.org/en/recrutement/index.php

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.
Location:
PMMH at ESPCI & Sorbonne U. and/or LPTMS at U. Paris-Saclay (Orsay)
Contact:
martin.lenz@espci.fr or martin.lenz@u-psud.fr
http://lptms.u-psud.fr/membres/mlenz/

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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
skills.

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 https://www.edpif.org/fr/recrutement/prop.php

You can also contact me directly at thorsten.emig@u-psud.fr or at 01.69.15.31.80.

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