The aim of the thesis is to perform attosecond photoemission spectroscopy on molecules in the gas and liquid phase exploiting a novel high repetition rate Ytterbium laser system. These studies will unveil the processes of photoionization of inner/outer shells and the dynamics of electron scattering in real time.
The main objective of this thesis is to model the mechanisms of rare earth ion emission from ionic liquids subjected to an intense electric field, in order to identify the conditions favorable to the emission of weakly complexed ions.
The aim is to establish rational criteria for the design of new ILIS sources suitable for the localized implantation of rare earths in photonic devices.
The thesis work will be based on large-scale molecular dynamics simulations, reproducing the emission region of a Taylor cone under an electric field.
The simulations will be compared with emission experiments conducted in parallel by the SIMUL group in collaboration with Orsay Physics TESCAN, using a prototype ILIS source doped with rare earths. Comparisons of measurements (mass spectrometry, energy distribution) will enable the models to be adjusted and the proposed mechanisms to be validated.
The presented project has two main objectives. The first one is the realization (building, calibration, data taking and data analysis) of a first experiment with the FALSTAFF detector in its configuration with two detection arms. In such a configuration, FALSTAFF will be able to detect in coincidence both fragments emitted by fast-neutron triggered fission reactions. These neutrons will be provided by the neutron beam of SPIRAL2-NFS in GANIL. The advantage of using direct kinematics is the ability to determine on an event-by-event basis the excitation energy of the fissioning nucleus by the measurement of the incident-neutron kinetic energy.
For this first experiment, we will have a uranium 235 target. 235U is the main source of fission neutrons in nuclear reactors and therefore at the heart of the system. Hence, the understanding of neutron-induced fission of 235U is essential and the rather exclusive data FALSTAFF will provide, with not only the identification of the fission fragments but also their kinematics will permit to reconstruct also the fissioning system. Such a measurmement in direct kinematics have never been done, to our knowledge, with the accuracy we are aiming at.
To perform this exepriment, we have improved and added detection capabilities to the FALSTAFF spectrometer, in particular with the financial support of the Région Normandie over the last two years. This experiment will be completed by a work to be done on a theoretical model developed by our collaborators of CEA-Cadarache. We will compare our detailled data with predictions of the model and have the model evolve, according to the laws of nuclear physics in order to obtain results from the model close to the data. Such a test of this model on as complete data as those we will obtain with FALSTAFF have never been done so far.
The ENUBET (Enhanced NeUtrino BEams from kaon Tagging) project aims to develop a monitored neutrino beam with a precisely known flux and flavor composition, enabling percent-level precision in neutrino cross-section measurements. This is achieved by instrumenting the decay tunnel to detect and identify charged leptons from kaon decays.
The PICOSEC Micromegas detector is a fast, double-stage micro-pattern gaseous detector that combines a Cherenkov radiator, a photocathode, and a Micromegas amplification structure. Unlike standard Micromegas, it operates with amplification also occurring in the drift region, where the electric field is even stronger than in the amplification gap. This configuration enables exceptional timing performance, with measured resolutions of about 12 ps for muons and ~45 ps for single photoelectrons, making it one of the fastest gaseous detectors ever developed.
Integrating large-area PICOSEC Micromegas modules in the ENUBET decay tunnel would provide sub-100 ps timing for lepton tagging, improving particle identification, reducing pile-up, and enhancing the association between detected leptons and their parent kaon decays — a key step toward precision-controlled neutrino beams.
Within the framework of this PhD work, the candidate will optimize and characterize 10 × 10 cm² PICOSEC Micromegas prototypes, and contribute to the design and development of larger-area detectors for the nuSCOPE experiment and the ENUBET hadron dump instrumentation.
Spin electronics, thanks to the additional degree of freedom provided by electron spin, enables the deployment of a rich physics of magnetism on a small scale, but also provides breakthrough technological solutions in the field of microelectronics (storage, memory, logic, etc.) as well as for magnetic field measurement.
In the field of life sciences and health, giant magnetoresistance (GMR) devices have demonstrated the possibility of measuring the very weak fields produced by excitable cells on a local scale (Caruso et al, Neuron 2017, Klein et al, Journal of Neurophysiology 2025).
Measuring the information contained in the magnetic component associated with neural currents (or magnetophysiology) can, in principle, provide a description of the dynamic, directional and differentiating neural landscape. It could pave the way for new types of implants, thanks to their immunity to gliosis and their longevity.
The current bottleneck is the very small amplitude of the signal produced (<1nT), which requires averaging the signal in order to detect it.
Tunnel magnetoresistances (TMR), in which a spin-polarised tunnel current is measured, offer sensitivity performance that is more than an order of magnitude higher than GMR. However, they currently have too high a level of low-frequency noise to be fully beneficial, particularly in the context of measuring biological signals.
The aim of this thesis is to push back the current limits of TMRs by reducing low-frequency noise, positioning them as break sensors for measuring very weak signals and exploiting their potential as amplifiers for small signals.
To achieve this objective, an initial approach based on exploring the materials composing the tunnel junction, in particular those of the so-called free magnetic layer, or on improving the crystallinity of the tunnel barrier, will be deployed. A second approach, consisting of studying the intrinsic properties of low-frequency noise, particularly in previously unexplored limits, at very low temperatures where intrinsic mechanisms are reached, will guide the most promising solutions.
Finally, the most advanced structures and approaches at the state of the art thus obtained will be integrated into devices that will provide the building blocks for going beyond the state of the art and offering new possibilities for spin electronics applications. These elements will also be integrated into systems for 2D (or even 3D) mapping of the activity of a global biological system (neural network) and for evaluating capabilities for clinical cases (such as epilepsy or motor rehabilitation).
It should be noted that these improved TMRs may have other applications in the fields of physical instrumentation, non-destructive testing, and magnetic imaging.
GANIL’s SPIRAL1 and SPIRAL2 facilities produce complex data that remain hard to interpret. SPIRAL2 faces instabilities in its superconducting cavities, while SPIRAL1 requires reliable isotope identification under noisy conditions.
This PhD will develop observer-based interpretable AI, combining physics models and machine learning to detect, explain, and predict anomalies. By embedding causal reasoning and explainability tools such as SHAP and LIME, it aims to improve the reliability and transparency of accelerator operations.
Understanding the electronic properties of valence electrons in nano-objects is not only of fundamental interest but also essential for the design of next-generation optoelectronic devices. In such systems, electron confinement in low-dimensional structures gives rise to unique properties.
These properties are inherently linked to fundamental characteristics of matter and the associated quantum fluctuations. More recently, concepts such as quantum entanglement and Fisher quantum information have been connected to spectroscopic properties. On the other hand, these spectroscopic properties can be probed through experimental techniques, including absorption, photoemission, and inelastic X-ray scattering.
Recently, we demonstrated that the widely used formalism to study isolated nano-objects was not adapted, and that it affected the calculated optical properties. We evidenced, theoretically and experimentally, that for the two-dimensional objects, the optical response contained, beyond the transverse contribution, a resonance coming from the plasmon, which corresponds to a longitudinal response. The role of the interfaces revealed to be determinant. The project of this year is to have a critical analysis of the optical properties of unidimensional objects.
Beyond the fundamental characterization of the 1D dielectric function, this research will explore its connection to quantum entanglement and Fisher quantum information—concepts that, to date, have not been investigated in low-dimensional systems.
Initially associated with neurodegenerative diseases, prion-like proteins (PrLPs) are now recognized as key physiological players in cellular plasticity and stress response. These proteins often contain an intrinsically disordered domain rich in glutamine and asparagine, known as a prion-like domain (PrLD), capable of switching between soluble, condensed, or amyloid states. Notable examples include CPEB in Aplysia, involved in synaptic memory, MAVS in antiviral defense, MED15 and FUS in transcriptional regulation and nucleocytoplasmic condensate dynamics, and ELF3 in plants, whose amyloid polymerization controls flowering and photoperiodic responses. In fungi, Sup35, Ure2p, and HET-s serve as experimental models of functional prions, demonstrating that reversible aggregation can act as a regulatory or adaptive mechanism. These conformational transitions are now viewed as adaptive molecular strategies rather than pathological anomalies.
This PhD project aims to trace the origin and diversification of prion-like proteins across eukaryotes, testing the hypothesis that major paleoclimatic crises have episodically promoted the emergence and duplication of genes encoding PrLDs through microsatellite expansion and transposable element activity. The project will combine large-scale phylogenomic analyses, PrLD domain detection, and modeling of selective pressures to map the key stages in the functional evolution of PrLPs and their links to stress tolerance.
Networks are crucial components of complex societies and underlie successful climate-energy strategies. Nevertheless they remain relatively understudied and insufficiently understood in their dynamics as well as in their relation to resource consumption and economic prosperity.
In this doctoral project, several historical cases of physical network will be explored from an industrial ecology standpoint and in relation to energy consumption. The project will address complexity in sociotechnical network structures and uses based on a complex systems modelling approach associating statistical physics (graph theory), geography and economic history. The project will mainly focus on the transportation and energy networks and their entanglement.
A first target will be railway networks that progressively grew during the 19th century in relation to coal extraction, trade and use. Railway networks are intertwined with early-industrial sociotechnical development and paved the way to the development of road networks in the 20th century in particular on the basis of complex oil networks. The study will address the dual role of railways and road networks in the transportation of both passengers and freight of energy and materials. The growth rates, interconnections and key metrics of these networks will be jointly analyzed and compared to an equivalent analysis of electricity grids which are currently under study by members of the PhD proposal team.
The thesis focuses on the development of rapid analysis methods for the detection and characterization of gravitational waves, particularly in the context of the upcoming LISA (Laser Interferometer Space Antenna) space mission planned by ESA around 2035. Data analysis involves several stages, one of the first being the rapid analysis “pipeline,” whose role is to detect new events and to characterize them. The final aspect concerns the rapid estimation of the sky position of the gravitational wave source and their characteristic time, such as the coalescence time in the case of black hole mergers. These analysis tools constitute the low-latency analysis pipeline.
Beyond its value for LISA, this pipeline also plays a crucial role in the rapid follow-up of events detected by electromagnetic observations (ground or space-based observatories, from radio waves to gamma rays). While fast analysis methods have been developed for ground-based interferometers, the case of space-borne interferometers such as LISA remains an area to be explored. Thus, a tailored data processing method will have to consider the packet-based data transmission mode, requiring event detection from incomplete data. From data affected by artifacts such as glitches, these methods must enable the detection, discrimination, and analysis of various sources.
In this thesis, we propose to develop a robust and effective method for the early detection of massive black hole binaries (MBHBs). This method should accommodate the data flow expected for LISA, process potential artifacts (e.g., non-stationary noise and glitches), and allow the generation of alerts, including a detection confidence index and a first estimate of the source parameters (coalescence time, sky position, and binary mass); such a rapid initial estimate is essential for optimally initializing a more accurate and computationally expensive parameter estimation.