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.
Triplet superconductors: from weak to strong spin-orbit coupling
Since the 1980s, several unconventional superconductors have been discovered, some of which exhibit triplet pairing (total spin S=1) that may lead to interesting topological properties. Unlike singlet superconductors, their order parameter is a vector depending on the spin components (S_z=-1,0,1) and is strongly influenced by the crystal symmetry and the spin–orbit coupling (SO).
The thesis aims to study the transition between weak and strong spin–orbit coupling in a triplet superconductor, using a minimal multiband model inspired by the material CdRh2As3, where a field-induced triplet phase was recently observed. This research will enable the calculation of the dynamic spin susceptibility and the identification of possible collective spin resonances, similar to those seen in superfluid He3.
The project will mainly rely on analytical field-theoretical methods applied to condensed matter. It is intended for candidates with a solid background in quantum mechanics, statistical physics, and solid-state physics.
Magneto-mechanical stimulation for the selective destruction of pancreatic cancer cells while sparing healthy cells
A novel approach for selectively destroying cancer cells is being developed through a collaboration between the BIOMICS biology laboratory and the SPINTEC magnetism laboratory, both part of the IRIG Institute. This method employs magnetic particles dispersed among cancer cells, which are set into low-frequency vibration (1–20 Hz) by an applied rotating magnetic field. The resulting mechanical stress induces controlled cell death (apoptosis) in the targeted cells.
The effect has been demonstrated in vitro across various cancer cell types—including glioma, pancreatic, and renal cells—in 2D cultures, as well as in 3D pancreatic cancer spheroids (tumoroids) and healthy pancreatic organoids. These 3D models, which more closely mimic the structure and organization of real biological tissues, facilitate the transition to in vivo studies and reduce reliance on animal models. Preliminary findings indicate that pancreatic cancer cells exhibit a higher affinity for magnetic particles and are more sensitive to mechanical stress than healthy cells, enabling selective destruction of cancer cells while sparing healthy tissue.
The next phase will involve confirming this specificity in mixed spheroids (containing both cancerous and healthy cells), statistically quantifying the results, and elucidating the mechanobiological mechanisms underlying cell death. These promising findings pave the way for an innovative biomedical approach to cancer treatment.
Regulation of gene expression by acetylation and lactylation of histone proteins
In eukaryotic cells, DNA wraps around histone proteins to form chromatin. Dynamic modification of histones by various chemical structures allows for fine regulation of gene expression. Alterations in these complex regulatory mechanisms are responsible for many diseases. Acetylation of histone lysines is known to induce gene expression. Other structures can be added to histones, whose effects on transcription remain largely unclear. Most of them, such as lactylation discovered in 2019, depend on cellular metabolism. We are studying this new modification in murine spermatogenesis: this process of cell differentiation is an ideal model for studying transcription regulation, due to dramatic changes in chromatin composition and gene expression patterns. We have established the distribution of acetylated and lactylated marks on three lysines of histone H3 across the genome. The aim of this thesis is to contribute to deciphering the “histone language,” first by studying the role of lactylations on the transcriptional program. Next, the prediction of chromatin states will be refined by integrating our new data with numerous available epigenomic data within neural network models.
Theoretical studies of orbital current and their conversion mechnism for leveraging spin-orbit torques based devices performances
The proposed PhD thesis aims at understanding and identifying the key parameters governing the conversion of orbital moments into spin currents, with the goal of enhancing the write efficiency of spin-orbit torque magnetic random-access memory (SOT-MRAM) devices. The work will employ a multiscale modeling approach comprising ab initio, tight-binding and atomistic calculations of the Orbital Hall Effect (OHE) and Orbital Rashba-Edelstein Effect (OREE). These phenomena exhibit larger magnitudes and diffusion lengths compared to their spin counterparts, Spin Hall Effect (SHE) and Rashba-Edelstein Effect (REE). Furthermore, they are present in a broader range of materials, including low-resistivity light metals. This opens very interesting opportunities for more efficient and conductive materials, potentially lifting the barriers limiting the technological deployment of SOT-MRAM.
This thesis will play a key role in a close collaboration between SPINTEC and LETI laboratories at CEA. The PhD student will conduct ab initio calculations at SPINTEC to unveil fundamental material characteristics to exploit the described orbitronic phenomena, and will construct multi-orbital Hamiltonians at LETI to calculate orbital and spin transport, in strong interaction/synergy with experimentalists working on SOT-MRAM development. The PhD will be co-supervised by M. Chshiev, K. Garello at Spintec and J. Li at LETI. This PhD project will be at the heart of collaborations with leading theoretical and experimental groups at national and international level.
Highly motivated candidates with a strong background in solid-state physics, condensed matter theory, and numerical simulations are encouraged to apply. The selected candidate will perform calculations using Spintec’s computational cluster, leveraging first-principles DFT-based packages and other simulation tools. Results will be rigorously analyzed, with opportunities for publication in international peer-reviewed journals.
Deep UV-LEDs based on digital alloys (GaN)n/(AlN)m
Context :
Group-III nitride semiconductors (GaN, AlN, InN) are renowned for their outstanding light emission properties. For more than two decades, they have powered the blue and white LEDs used worldwide, thanks to highly efficient InGaN quantum wells (external quantum efficiency > 80%). In contrast, UV LEDs based on AlGaN quantum wells are still very inefficient (< 10%) and only recently became commercially available. Overcoming this limitation is a key challenge in optoelectronics: achieving efficient deep-UV emission (220–280 nm) would enable high-performance bactericidal applications such as water purification, surface sterilization, and virus inactivation.
Recently, two breakthrough concepts are promising to explore for UV-LEDs:
1. Deep-UV emission from GaN monolayers in AlN: Grow a few atomic monolayers (MLs) of GaN embedded in an AlN matrix. This extreme quantum confinement leads to deep-UV emission down to 220 nm. High emission efficiency is expected due to strong exciton binding, stable even at room temperature
2. Enhanced doping using graded digital GaN/AlN alloys: Use graded digital alloys (GaN)?/(AlN)? where n and m are the number of atomic layers. This architecture enables efficient n- and especially p-type doping, which is a major bottleneck in AlGaN. GaN is much easier to dope than AlN, making this approach very promising for device fabrication.
Scientific Targets :
The aim is to master monolayer growth using MOVPE (metal-organic vapor phase epitaxy), the most industrially relevant technique :
- M2 project: develop the growth of GaN monolayers on AlN substrates, study their deep-UV emission properties, and optimize growth conditions for self-limited single-layer deposition.
- PhD continuation: design and fabricate doped digital GaN/AlN alloys to build the first efficient deep-UV LEDs based on this architecture.
Lab background and collaboration:
The group has long-standing expertise in visible and UV light emission from nitride nanowires. We have already demonstrated 280 nm emission from (GaN)?/(AlGaN)? digital alloys, proving the viability of this approach. The project will be highly experimental (epitaxy, advanced structural and optical characterization) and conducted in close collaboration with Institut Néel for cathodoluminescence analysis and device processing.
Why join this project ?
Gain expertise in epitaxy, semiconductor physics, and optoelectronics. Work in a dynamic, collaborative environment with strong ties to industry. Contribute to the development of the next generation of deep-UV LEDs.
Real-space fitting of flexible molecular structures in high-speed AFM topographic movies
Structural biology seeks to understand the function of macromolecules by determining the precise position of their atoms. Its traditional methods (X-ray crystallography, NMR, electron microscopy), although effective, offer a static view of macromolecules, limiting the study of their dynamics. A new paradigm is emerging: integrative structural biology, combining several techniques to capture, among other things, molecular dynamics. However, despite improvements in femtosecond serial crystallography, molecular dynamics simulations, and cryo-electron tomography, current methods struggle to reach the functional time scale (milliseconds to seconds).
The advent of new scanning probe microscopy, and in particular the recent development of high-speed atomic force microscopy (HS-AFM), allows molecular movements to be observed on the millisecond scale, but lacks the atomic resolution to revolutionize structural biology. The objective of the proposed topic is to further exploit the use of HS-AFM by modeling detailed atomic structures at the heart of the images obtained. The tasks will be both biophysical and computational, involving the improvement of the existing AFM-Assembly tool, which allows direct spatial adjustment of the atomic coordinates of the target molecule under AFM topography. The aim is to apply this protocol to a new type of big data, namely topographical movies obtained by high-speed AFM.
The thesis will be conducted at the Institute of Structural Biology in Grenoble, within the Methods and Electron Microscopy (MEM) group of the Grenoble Interdisciplinary Research Institute (IRIG). It will be carried out in collaboration with the DyNaMo laboratory in Marseille, which specializes in high-speed AFM data acquisition, as part of a joint ANR funding application.
The scientific interest of the project is major for modern integrative structural biology. The great scientific challenge of the coming years in structural biology is the study and analysis of molecular dynamics, in order to move beyond the current paradigm (instantaneous photography) and participate in the emergence of a new paradigm (real-time movie).
A new altermagnetic material with remarkable properties for spintronics
Altermagnets represent a new class of magnetic materials that uniquely combine the advantages of ferromagnets (spin polarization of electric currents) and antiferromagnets (robustness against magnetic fields and ultrafast spin dynamics). As part of an international collaboration, we have experimentally discovered one of the very first and still rare altermagnets, Mn5Si3, thereby opening the way for new fundamental and applied research. Until now, Mn5Si3 has mainly been synthesized by molecular beam epitaxy, a high-precision technique but one that presents limitations for broader studies. Our goal is to develop the growth of Mn5Si3 using high-temperature sputtering, a more versatile and industry-compatible method, in order to explore and demonstrate its exceptional spin properties.
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.