Surface technologies for enhanced superconducting Qubits lifetimes
Materials imperfections in superconducting quantum circuits—in particular, two-level-system (TLS) defects—are a major source of decoherence, ultimately limiting the performance of qubits. Thus, identifying the microscopic origin of possible TLS defects in these devices and developing strategies to eliminate them is key to superconducting qubit performance improvement. This project proposes an original approach that combines the passivation of the superconductor’s surface with films deposited by Atomic Layer Deposition (ALD), which inherently have lower densities of TLS defects, and thermal treatments designed to dissolve the initially present native oxides. These passivating layers will be tested on 3D Nb resonators than implemented in 2D resonators and Qubits and tested to measure their coherence time. The project will also perform systematic material studies with complementary characterization techniques in order to correlate improvements in qubit performances with the chemical and crystalline alteration of the surface.
Euclid Weak Lensing Cluster Cosmology inference
Galaxy clusters, which form at the intersection of matter filaments, are excellent tracers of the large-scale matter distribution in the Universe and are a valuable source of information for cosmology.
The sensitivity of the Euclid space mission (launch in 2023) allow blind detection of galaxy clusters through gravitational lensing (i.e. directly linked to the projected total mass). Combined with its wide survey area (14,000 deg²), Euclid should allow the construction of a galaxy cluster catalogue that is unique in both its size and selection properties.
In contrast to existing cluster catalogues, which are typically based on baryonic content (e.g., X-ray emission from intra-cluster gas, the Sunyaev-Zel’dovich effect in the millimeter regime, or optical emission from galaxies), a catalogue derived from gravitational lensing is directly sensitive to the total mass of the clusters. This makes it truly representative of the underlying cluster population, a significant advantage for both galaxy cluster studies and cosmology.
In this context, we have developed a multi-scale detection method specifically designed to identify galaxy clusters based only on their gravitational lensing signal, which has been pre-selected to produce the Euclid cluster catalogue.
The goal of this PhD project is to build and characterize the galaxy cluster catalogue identified via weak lensing in the data collected during the first year of Euclid observations (DR1), based on this detection method. The candidate will derive cosmological constraints from the modelling of the cluster abundance, using the classical Bayesian framework, and will also investigate the potential of Simulation-Based Inference (SBI) methods for cosmological inference.
From Detector to Discovery: Constructing the ATLAS Inner Tracker and Probing Higgs Physics at the HL-LHC
This PhD project combines work on the construction of the new Inner Tracker (ITk) for the ATLAS experiment and an analysis of ATLAS sensitivity at the High-Luminosity LHC (HL-LHC) to key processes related to Higgs boson physics using ITk. The candidate will take part in the development, operation, and optimization of the test benches for ITk pixel modules at CEA. Together with two other partner laboratories in the Paris region, CEA will assemble and test about 20% of the ITk pixel modules. The student will contribute to the commissioning of the detector at CERN. The candidate will also carry out HL-LHC sensitivity studies of the interactions between the Higgs boson and the top quark, including for instance a CP-violation analysis in the ttH channel and an analysis of tH production, a process particularly sensitive to the Higgs–top and Higgs–W couplings. The first two years of the PhD are expected to be based at CEA Saclay, while the last year will be based at CERN.
Joint simulation-based inference of tSZ maps and Euclid's weak lensing
Context:
The Euclid mission will provide weak lensing measurements with unprecedented precision, which have the potential to revolutionise our understanding of the Universe. However, as the statistical uncertainties decrease, controlling systematic effects becomes even more crucial. Among these, baryonic feedback, which redistributes gas within galaxies and clusters, remains one of the key astrophysical systematic effects limiting Euclid’s ability to constrain the equation of state of dark energy. Understanding baryonic feedback is one of the urgent challenges of cosmology today.
The thermal Sunyaev-Zel’dovich (tSZ) effect provides a unique window into the baryonic component of the Universe. This effect arises from the scattering of cosmic microwave background (CMB) photons by hot electrons in galaxy groups and clusters. This is the same hot gas that has been redistributed by baryonic feedback and is particularly relevant for weak lensing cosmology. The cross-correlation between tSZ and weak lensing (WL) probes how baryons trace and modify the cosmic structures, allowing joint constraints on cosmology and baryonic physics.
Most current tSZ-WL analyses rely on fitting angular power spectra under the assumption of a Gaussian likelihood. However, the tSZ signal is highly non-Gaussian, as it traces the massive structures of the Universe, and the power spectra fail to fully capture the information in the data. To unlock the scientific potential of the tSZ-WL analyses, it is essential to move beyond these simplifying assumptions.
PhD thesis:
The goal of this PhD project is to develop a novel simulation-based framework to jointly analyse tSZ and Euclid’s WL data. This framework will combine physically motivated forward models with advanced statistical and machine-learning techniques to provide accurate measurements of baryonic feedback and cosmological parameters. By jointly analysing tSZ and WL measurements, this project will increase the accuracy of Euclid’s cosmological analyses and improve our understanding of the dark matter-baryon connection.
Cosmology with the Lyman-alpha forest from the DESI cosmological survey.
We use the large-scale distribution of matter in the universe to test our cosmological models. This is primarily done using baryon acoustic oscillations (BAO), which are measured in the two-point correlation function of this distribution. However, the entire matter field contains information at various scales, allowing us to better constrain our models than BAO alone. At redshifts greater than 2, the Lyman-alpha forest is the best probe of this matter distribution. The Lyman-alpha forest is a set of absorption lines measured in the spectra of distant sources. The large DESI spectroscopic survey has collected approximately one million of these spectra. Using the partial data set "DR2," we measured the BAO with an accuracy of 0.7%, which strongly constrains the expansion rate of the universe during the first billion years of its evolution.
This thesis aims to exploit the full set of large-scale Lyman-alpha data from DESI to obtain the strongest constraints on cosmological models possible. First, the student will apply a method known as reconstruction to improve the accuracy of BAO measurements by exploiting information from the matter density field. For the remainder of the thesis, the student will implement a new method known as simulation-based inference. Similar efforts have been carried out in our group with DESI galaxies. In this approach, the entire matter field is used directly to estimate cosmological parameters, particularly dark energy. Thus, the student will make an important contribution to DESI's final cosmological measurements with Lyman-alpha.
An internship is preferred before beginning this thesis.
Unbiased Shear Estimation for Euclid with Automatically Differentiable and GPU Accelerated Modeling
This PhD project focuses on achieving unbiased measurements of weak gravitational lensing — the tiny distortions in galaxy shapes caused by the matter along the line of sight. This technique is key to studying dark matter, dark energy, and gravity, and lies at the heart of the Euclid space mission launched in 2023. Traditional shape-measurement methods introduce systematic biases in shear estimation. The goal of this PhD is to develop and extend an innovative forward-modelling approach that directly infers the shear by simulating realistic galaxy images using deep-learning architectures. The student will adapt this framework to real Euclid data, accounting for the complexity of the Science Ground Segment (SGS) and implementing GPU-accelerated and high-performance computing solutions to scale to the full sky coverage. The project is timely, coinciding with Euclid’s first public data release in 2026. The expected outcome is a more accurate and robust shear estimation method, enabling the next generation of precision cosmology analyses.
From Cosmic Web to Galaxies: Tracing Gas Accretion at High Redshift through Observations and Simulations
This thesis aims to develop an integrated understanding of high-redshift galaxies within their large-scale structures. We will investigate how feedback and nuclear activity from these galaxies affect their environments by coupling observational data with cosmological simulations.
Our primary objectives are to:
1. Advance the diagnostic capabilities for studying diffuse gas.
2. Test and validate current paradigms of gas accretion.
Our observational work will utilize new data from Keck and the Very Large Telescope on Lyman-alpha halos around massive groups and clusters at z>2, which are already largely in hand. We will also incorporate a growing body of data from the James Webb Space Telescope (JWST) on the same targets to reveal the properties of galaxies and their active galactic nuclei (AGNs).
On the theoretical side, we will use publicly available results from the TNG100, HORIZON5, and CALIBRE simulations to understand galaxy evolution, learning from both the successes and failures in the comparison with observations. Ultimately, this will allow us to inform new, high-fidelity simulations of the circum-galactic medium, designed specifically to constrain gas accretion processes.
This research directly supports our long-term goal of preparing for the exploitation of BlueMUSE, a new instrument being built for the VLT, in which we participate. It will also address one of the key open questions in astrophysics, as highlighted by the Astro2020 Decadal Survey.
Dimensionality reduction method applied to the deformed coupled cluster ab initio many-body method
The theoretical description from first principles, i.e. in a so-called ab initio manner, of atomic nuclei containing more than 12 nucleons has only recently become possible thanks to the crucial developments in many-body theory and the availability of increasingly powerful high-performance computers. These ab initio techniques are successfully applied to study the structure of nuclei, starting from the lightest isotopes and now reaching all medium-mass nuclei containing up to about 80 nucleons. The extension to even heavier systems requires decisive advances in terms of storage cost and computation time induced by available many-body methods. In this context, the objective of the thesis is to develop the dimensionality reduction method based on the factorization of tensors involved in the non-perturbative many-body theory known as deformed coupled cluster (dCC). The proposed work will exploit the latest advances in nuclear theory, including the use of nuclear potentials from chiral effective field theory and renormalization group techniques, as well as high-performance computing resources and codes.
Machine-learning methods for the cosmological analysis of weak- gravitational lensing images from the Euclid satellite
Weak gravitational lensing, the distortion of the images of high-redshift galaxies due to foreground matter structures on large scales, is one of the most promising tools of cosmology to probe the dark sector of the Universe. The statistical analysis of lensing distortions can reveal the dark-matter distribution on large scales, The European space satellite Euclid will measure cosmological parameters to unprecedented accuracy. To achieve this ambitious goal, a number of sources of systematic errors have to be quanti?ed and understood. One of the main origins of bias is related to the detection of galaxies. There is a strong dependence on local number density and whether the galaxy's light emission overlaps with nearby objects. If not handled correctly, such ``blended`` galaxies will strongly bias any subsequent measurement of weak-lensing image distortions.
The goal of this PhD is to quantify and correct weak-lensing detection biases, in particular due to blending. To that end, modern machine- and deep-learning algorithms, including auto-differentiation techniques, will be used. Those techniques allow for a very efficient estimation of the sensitivity of biases to galaxy and survey properties without the need to create a vast number of simulations. The student will carry out cosmological parameter inference of Euclid weak-lensing data. Bias corrections developed during this thesis will be included a prior in galaxy shape measurements, or a posterior as nuisance parameters. This will lead to measurements of cosmological parameters with a reliability and robustness required for precision cosmology.
Cosmological parameter inference using theoretical Wavelet statistics predictions
Launched in 2023, the Euclid satellite is surveying the sky in optical and infrared wavelengths to create an unprecedented map of the Universe's large-scale structure. A cornerstone of its mission is the measurement of weak gravitational lensing—subtle distortions in the shapes of distant galaxies. This phenomenon is a powerful cosmological probe, capable of tracing the evolution of dark matter and helping to distinguish between dark energy and modified gravity theories.
Traditionally, cosmologists have analyzed weak lensing data using second-order statistics (like the power spectrum) paired with a Gaussian likelihood model. This established approach, however, faces significant challenges:
- Loss of Information: Second-order statistics fully capture information only if the underlying matter distribution is Gaussian. In reality, the cosmic web is highly structured, with clusters, filaments, and voids, making this approach inherently lossy.
- Complex Covariance: The method requires estimating a covariance matrix, which is both cosmology-dependent and non-Gaussian. This necessitates running thousands of computationally intensive N-body simulations for each model, a massive and often impractical undertaking.
- Systematic Errors: Incorporating real-world complications—such as survey masks, intrinsic galaxy alignments, and baryonic feedback—into this framework is notoriously difficult.
In response to these limitations, a new paradigm has emerged: likelihood-free inference via forward modelling. This technique bypasses the need for a covariance matrix by directly comparing real data to synthetic observables generated from a forward model. Its advantages are profound: it eliminates the storage and computational burden of massive simulation sets, naturally incorporates high-order statistical information, and can seamlessly integrate systematic effects. However, this new method has its own hurdles: it demands immense GPU resources to process Euclid-sized surveys, and its conclusions are only as reliable as the simulations it uses, potentially leading to circular debates if simulations and observations disagree.
A recent breakthrough (Tinnaneni Sreekanth, 2024) offers a compelling path forward. This work provides the first theoretical framework to directly predict key wavelet statistics of weak lensing convergence maps—exactly the kind Euclid will produce—for any given set of cosmological parameters. It has been shown in Ajani et al (2021) that the wavelet coefficient L1-norm is extremely powerful to constraint the cosmological parameters. This innovation promises to harness the power of advanced, non-Gaussian statistics without the traditional computational overhead, potentially unlocking a new era of precision cosmology. We have demonstrated that this theoretical prediction can be used to build a highly efficient emulator (Tinnaneri Sreekanth et al, 2025), dramatically accelerating the computation of these non-Gaussian statistics. However, it is crucial to note that this emulator, in its current stage, provides only the mean statistic and does not include cosmic variance. As such, it cannot yet be used for full statistical inference on its own.
This PhD thesis aims to revolutionize the analysis of weak lensing data by constructing a complete, end-to-end framework for likelihood-free cosmological inference. The project begins by addressing the core challenge of stochasticity: we will first calculate the theoretical covariance of wavelet statistics, providing a rigorous mathematical description of their uncertainty. This model will then be embedded into a stochastic map generator, creating realistic mock data that captures the inherent variability of the Universe.
To ensure our results are robust, we will integrate a comprehensive suite of systematic effects—such as noise, masks, intrinsic alignments, and baryonic physics—into the forward model. The complete pipeline will be integrated and validated within a simulation-based inference framework, rigorously testing its power to recover unbiased cosmological parameters. The culmination of this work will be the application of our validated tool to the Euclid weak lensing data, where we will leverage non-Gaussian information to place competitive constraints on dark energy and modified gravity.
References
V. Ajani, J.-L. Starck and V. Pettorino, "Starlet l1-norm for weak lensing cosmology", Astronomy and Astrophysics, 645, L11, 2021.
V. Tinnaneri Sreekanth, S. Codis, A. Barthelemy, and J.-L. Starck, "Theoretical wavelet l1-norm from one-point PDF prediction", Astronomy and Astrophysics, 691, id.A80, 2024.
V. Tinnaneri Sreekanth, J.-L. Starck and S. Codis, "Generative modeling of convergence maps based in LDT theoretical prediction", Astronomy and Astrophysics, 701, id.A170, 2025.