Transport in runaway electron companion plasmas: impact on mitigation and extrapolation to ITER

Disruptions are abrupt interruptions of plasma discharges in tokamaks. They are due to instabilities leading to the loss of thermal energy and magnetic energy of the plasma over periods of the order of a few tens of milliseconds. Disruptions can generate so-called relativistic runaway electron beams reaching energies up to several MeV and potentially carrying a large part of the initial current. It is crucial to control or stop them to ensure a reliable operation of future tokamaks such as ITER. The proposed thesis project focuses on the mitigation of runaway electrons by massive injection of deuterium or hydrogen into the beam. This scenario leads to a drastic decrease in the energy deposited on the wall by the runaway electrons, through two phenomena: a magnetohydrodynamic instability and the absence of regeneration of the runaway electrons in the final loss of the plasma current. These two conditions are obtained when the plasma created by the interaction between the runaway electron beam and the neutral gas remains cold enough to recombine. The recombination mechanism relies on energy transport processes by the neutrals and a decrease in the interaction between the runaway electrons and the background plasma. Limits to this scheme were found on current tokamaks; they must be understood in order to extrapolate to future machines. The first part of the thesis will focus on the characterization of the cold plasma: density profiles, deuterium/hydrogen or heavy impurity concentration, current profile. We will be particularly interested in the quantities related to transport phenomena in the plasma: heat conduction, particle diffusion or radiation transport. This experimental characterization will quickly call upon numerical modelling to confirm the role of the various transport mechanisms in keeping the conditions required for the dissipation of the beam without damage. An extrapolation towards ITER will then be considered via simulations.

Dynamic interplay of Rad51 nucleoprotein filament-associated proteins - Involvement in the regulation of homologous recombination

Homologous recombination (HR) is an important repair mechanism for DNA double-strand breaks induced by ionizing radiation. A key step in HR is the formation of Rad51 nucleoprotein filaments on the single-stranded DNA that is generated from these breaks. We were the first to show, using yeast as a model, that a tight control of the formation of these filaments is essential for HR not to induce chromosomal rearrangements by itself (eLife 2018, Cells 2021). In humans, the functional homologs of the yeast control proteins are tumor suppressors. Thus, the control of HR seems to be as important as the mechanism of HR itself. Our project involves the use of new molecular tools that allow a breakthrough in the study of these controls. We will use a functional fluorescent version of the Rad51 protein, first developed by our collaborators A. Taddei (Institut Curie), R. Guérois and F. Ochsenbein (I2BC, Joliot, CEA). This major advance will allow us to observe the influence of regulatory proteins on DNA repair by microscopy in living cells. We have also developed highly accurate structural models of control protein complexes associated with Rad51 filaments. We will adopt a multidisciplinary approach based on genetics, molecular biology, biochemistry, and protein structure in collaboration with W.D. Heyer (University of California, Davis, USA), to understand the function of the regulators of Rad51 filament formation. The description of the organization of these proteins with Rad51 filaments will allow us to develop new therapeutic approaches.

Perovskite ferroelectric oxynitride thin films with tunable properties

N-doped oxides and/or oxinitrides constitute a booming class of compounds with a broad spectrum of useable properties and in particular for novel technologies of carbon-free energy production. Indeed, the insertion of nitrogen into the crystal lattice of a semiconductor oxide allows, in principle, to modulate the value of its band gap or to introduce additional electronic states and thus to obtain new functionalities and optical properties. The production of oxynitride single crystalline thin films is highly challenging. In this essentially experimental thesis work, thin films of oxynitrides will be developed by atomic plasma-assisted molecular beam epitaxy. We will start from BaTiO3, which synthesis is well mastered in the laboratory, to realize co-dopings with nitrogen and compensating metals in order to preserve the neutrality of the elementary unit cell. The resulting structures will be studied for their chemical compositions, crystalline structures and ferroelectric characteristics. These observations will be correlated with their performance for the photo-electrolysis of water, which allows the virtuous production of hydrogen. Finally, the corrosion resistance of these new materials will also be studied.
The student will acquire skills in a wide range of ultra-high vacuum techniques, molecular beam epitaxy growth, clean room lithography, ferroelectric measurements and photo-electrolysis of water, as well as in state-of-the-art synchrotron radiation techniques.

DNA METHYLATION AND THE 3D GENOME ORGANIZATION OF BACTERIA

DNA methylation in bacteria has been traditionally studied in the context of antiparasitic defense and as part of the innate immune discrimination between self and non-self DNA. However, sequencing advances that allow genome-wide analysis of DNA methylation at the single-base resolution are nowadays expanding and have propelled a modern epigenomic revolution in our understanding of the extent, evolution, and physiological relevance of methylation. Typically, the first step in studying the functional impacts of bacterial DNA methylation is to compare global gene expression between wild-type (WT) and methyltransferase (MTase) mutant strains. Several studies using RNA-seq for such comparisons have shown that perturbation of a single DNA MTase often results in tens, hundreds, and sometimes thousands of differentially expressed (DE) genes. According to the local competition model, competitive binding between an MTase and other DNA-binding proteins (e.g.: transcription factors) at specific motif sites affects transcription of a nearby gene, leading to phenotypic variation within the bacterial population. However, while in some cases the regulatory effects of MTases can be conclusively traced to methylation at the promoters of target genes, the large majority (>90%) of DE genes do not have methylated sites in their promoter regions, which implies that the local competition model does not apply to most DE genes. Another possibility is that the methylation status at individual motif sites might regulate the expression of a transcription factor, causing a broad downstream shift in the expression of its target genes. Yet, the latter is also not sufficiently explanatory for such a large number of DE genes. One hypothesis relates to the effect of DNA methylation on the chromosome topology whereby methylation induces structural changes that alter the repertoire of genes exposed to the cellular transcriptional machinery. We have recently identified CamA, a core MTase of Clostridioides difficile methylating at CAAAAA, with a
role in biofilm formation, sporulation, and in-vivo transmission. Moreover, in a subsequent large-scale analysis, we found that CamA was just the tip of the iceberg, with 45% of Genbank’s bacterial species containing at least one core or quasi-core MTase, which shows that the latter are abundant and suggests that their epigenetic modifications are likely important and frequent. On top of this, S-adenosyl-l-methionine (SAM) analogues were found to successfully inhibit CamA, in what represents a substantial first step in generating potent and selective epigenetically targeted therapeutics that can be exploited as new antimicrobials.
In this PhD project proposal, the successful candidate is asked to decipher the interplay between bacterial methylation, spatial genome organization and gene expression by answering the following questions: i) does methylation alter chromosomal interaction domains? ii) are DE genes and/or target methylation motifs enriched in changeable chromosomal interaction domain boundaries? iii) Can we tinker the methylome (globally or locally) to repress certain human pathogens? He / she will use Hi-C and long-read sequencing technologies combined with microbial genetics, and comparative genomics to broadly leverage the field of microbial epigenomics.

Study of the links between the dysregulations of metabolism and epigenetics marks in Huntington’s disease

We want to focus on epigenetic dysregulation in Huntington’s Disease (HD), a pathogenic mechanism implicated in accelerated aging of striatal neurons. Specifically, we will investigate the interplay between altered energy metabolism and epigenetic impairment in HD striatal neurons to identify new targets/pathways for disease-modifying intervention. We aim to obtain detailed maps of histone post-translational modifications (PTMs), especially of methylations, acetylation and the recently described lactylation, which might be critical in the HD brain. Indeed, these PTMs are tightly regulated by the metabolic status of the cells. We will use proteomics which is the best suited approach to identify and quantify multiple protein PTMs. We consider working on the striatum of WT, R6/1 transgenic mice and the more progressive Q140 knock in model at various stages of disease, to assess evolution of histone PTMs and metabolism with aging. Additionally, to get a dynamic view of the links between metabolic and epigenetic imbalance in HD, we will inject intraperitoneally HD mice and controls with 13C-glucose and analyze over a time course the incorporation of 13C into histone PTMs. Finally, acetyl-CoA, the precursor for histone lysine acetylation, has been shown to be locally produced in the nucleus, by either acetyl-CoA synthetase 2 (ACSS2), ATP-citrate lyase (ACLY) or the pyruvate dehydrogenase complex. Regarding lactylation, it is currently unknown where, and by which enzymes, the pool of lactate used for modifying histone lysines by lactylation is produced. ACSS2 is a very good candidate, as it can catalyze the production of acyl-CoA molecules from the corresponding short chain fatty acids (SCFA). To address the question of the production of metabolites in the vicinity of chromatin in striatal cells, we will use epigenomics (ChIPseq or CUT&tag) to get the genomic distribution of ACSS2 and ACLY and compare it to distributions of acetyl and lactyl histone marks.

Perovskite devices for solar hydrogen production

Project Overview:
The PhD thesis is part of the ICARUS European project, aiming to develop efficient solar energy conversion systems for a carbon-neutral future. The project focuses on integrating photoelectrochemical (PEC) water splitting and photovoltaic (PV) power generation.

Key Objectives:
•Develop innovative metal halide perovskite solar cells with tunable bandgaps for broader light absorption.
•Optimize printed carbon-based solar cells and scaffolds for improved conductivity, mechanical resistance, and durability.
•Incorporate innovative carbon counter electrodes into perovskite devices.
•Upscale and manufacture solar modules.
•Integrate the developed modules into a final PEC prototype.

Research Focus:
The PhD candidate will primarily focus on:
•Printed carbon-based solar cells: Optimizing ink properties, investigating the behavior of printed conductive ink under various conditions, and characterizing conductivity and mechanical resistance.
•Perovskite devices: Incorporating innovative carbon counter electrodes and evaluating their performance and stability.
•Module manufacturing: Upscaling and manufacturing solar modules based on the developed technologies.
•PEC prototype integration: Contributing to the final integration of the PEC prototype.

Expected Outcomes:
The research is expected to contribute to the development of highly efficient and sustainable solar energy conversion systems, supporting the transition to a carbon-neutral future. The findings will have implications for both academic research and industrial applications.

Giant magnetoresistance resistors for local characterization of surface magnetic state: towards Non-Destructive Testing (NDT) applications

CIFRE thesis in the field of non-destructive testing using magnetic sensors in collaboration with 3 partners:

Laboratoire de Nanomagnétisme et Oxyde (SPEC/LNO) du CEA Paris-Saclay
Laboratoire de Génie Electrique et Ferroélectricité (LGEF) de l’INSA Lyon
Entreprise CmPhy

Exploring the High-Frequency fast Electron-Driven Instabilities towards application to WEST

In current tokamaks, the electron distribution is heavily influenced by external heating systems, like Electron Cyclotron Resonance Heating (ECRH) or Lower Hybrid (LH) heating, which generate a large population of fast electrons. This is expected also in next-generation tokamaks, such as ITER, where a substantial part of input power is deposited on electrons. A significant population of fast electrons can destabilize high-frequency instabilities, including Alfvén Eigenmodes (AEs), as observed in various tokamaks. However, this phenomenon remains understudied, especially regarding the specific resonant electron population triggering these instabilities and the impact of electron-driven AEs on the multi-scale turbulence dynamics in the plasma complex environment.
The PhD project aims to explore the physics of high-frequency electron-driven AEs in realistic plasma conditions, applying insights to WEST experiments for in-depth characterization of these instabilities. The candidate will make use of advanced numerical codes, whose expertise is present at the IRFM laboratory, to analyze realistic plasma conditions with fast-electron-driven AE in previous experiments, to grasp the essential physics at play. Code development will also be necessary to capture key aspects of this physics. Once such a knowledge is established, predictive modeling for the WEST environment will guide experiments to observe these instabilities.
Based at CEA Cadarache, the student will collaborate with different teams, from the theory and modeling group to WEST experimental team, gaining diverse expertise in a stimulating environment. Collaborations with EUROfusion task forces will further provide an enriching international experience.

Generative AI for Robust Uncertainty Quantification in Astrophysical Inverse Problems

Context
Inverse problems, i.e. estimating underlying signals from corrupted observations, are ubiquitous in astrophysics, and our ability to solve them accurately is critical to the scientific interpretation of the data. Examples of such problems include inferring the distribution of dark matter in the Universe from gravitational lensing effects [1], or component separation in radio interferometric imaging [2].

Thanks to recent deep learning advances, and in particular deep generative modeling techniques (e.g. diffusion models), it now becomes not only possible to get an estimate of the solution of these inverse problems, but to perform Uncertainty Quantification by estimating the full Bayesian posterior of the problem, i.e. having access to all possible solutions that would be allowed by the data, but also plausible under prior knowledge.

Our team has in particular been pioneering such Bayesian methods to combine our knowledge of the physics of the problem, in the form of an explicit likelihood term, with data-driven priors implemented as generative models. This physics-constrained approach ensures that solutions remain compatible with the data and prevents “hallucinations” that typically plague most generative AI applications.

However, despite remarkable progress over the last years, several challenges still remain in the aforementioned framework, and most notably:

[Imperfect or distributionally shifted prior data] Building data-driven priors typically requires having access to examples of non corrupted data, which in many cases do not exist (e.g. all astronomical images are observed with noise and some amount of blurring), or might exist but may have distribution shifts compared to the problems we would like to apply this prior to.
This mismatch can bias estimations and lead to incorrect scientific conclusions. Therefore, the adaptation, or calibration, of data-driven priors from incomplete and noisy observations becomes crucial for working with real data in astrophysical applications.

[Efficient sampling of high dimensional posteriors] Even if the likelihood and the data-driven prior are available, correctly sampling from non-convex multimodal probability distributions in such high-dimensions in an efficient way remains a challenging problem. The most effective methods to date rely on diffusion models, but rely on approximations and can be expensive at inference time to reach accurate estimates of the desired posteriors.

The stringent requirements of scientific applications are a powerful driver for improved methodologies, but beyond the astrophysical scientific context motivating this research, these tools also find broad applicability in many other domains, including medical images [3].

PhD project
The candidate will aim to address these limitations of current methodologies, with the overall aim to make uncertainty quantification for large scale inverse problems faster and more accurate.
As a first direction of research, we will extend recent methodology concurrently developed by our team and our Ciela collaborators [4,5], based on Expectation-Maximization, to iteratively learn (or adapt) diffusion-based priors to data observed under some amount of corruption. This strategy has been shown to be effective at correcting for distribution shifts in the prior (and therefore leading to well calibrated posteriors). However, this approach is still expensive as it requires iteratively solving inverse problems and retraining the diffusion models, and is critically dependent on the quality of the inverse problem solver. We will explore several strategies including variational inference and improved inverse problem sampling strategies to address these issues.
As a second (but connected) direction we will focus on the development of general methodologies for sampling complex posteriors (multimodal/complex geometries) of non-linear inverse problems. Specifically we will investigate strategies based on posterior annealing, inspired from diffusion model sampling, applicable in situations with explicit likelihoods and priors.
Finally, we will apply these methodologies to some challenging and high impact inverse problems in astrophysics, in particular in collaboration with our colleagues from the Ciela institute, we will aim to improve source and lens reconstruction of strong gravitational lensing systems.
Publications in top machine learning conferences are expected (NeurIPS, ICML), as well as publications of the applications of these methodologies in astrophysical journals.

References
[1] Benjamin Remy, Francois Lanusse, Niall Jeffrey, Jia Liu, Jean-Luc Starck, Ken Osato, Tim Schrabback, Probabilistic Mass Mapping with Neural Score Estimation, https://www.aanda.org/articles/aa/abs/2023/04/aa43054-22/aa43054-22.html

[2] Tobías I Liaudat, Matthijs Mars, Matthew A Price, Marcelo Pereyra, Marta M Betcke, Jason D McEwen, Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging, RAS Techniques and Instruments, Volume 3, Issue 1, January 2024, Pages 505–534, https://doi.org/10.1093/rasti/rzae030

[3] Zaccharie Ramzi, Benjamin Remy, Francois Lanusse, Jean-Luc Starck, Philippe Ciuciu, Denoising Score-Matching for Uncertainty Quantification in Inverse Problems, https://arxiv.org/abs/2011.08698

[4] François Rozet, Gérôme Andry, François Lanusse, Gilles Louppe, Learning Diffusion Priors from Observations by Expectation Maximization, NeurIPS 2024, https://arxiv.org/abs/2405.13712

[5] Gabriel Missael Barco, Alexandre Adam, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur, Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems, https://arxiv.org/abs/2407.17667

Very high energy electrons radiotherapy with beams from a wakefield accelerator

Research objectives:
Use numerical modelling to optimize the properties of laser-plasma accelerators in the 50 MeV-200 MeV range for VHEE radiotherapy:
(i) optimize the properties of a laser-plasma accelerator (energy spread, divergence) with electron beams injected from a plasma-mirror injector using the WarpX and HiPACE++ codes.
(ii) Study the impact of such electron beams on DNA using Geant4DNA.

This numerical modelling will then be used to guide/design/interpret experiments of radiobiology on in-vitro biological samples that are planned at our in-house 100 TW laser facility at CEA during the project. This will be carried out in the context of research project FemtoDose funded by the French National Research Agency.

The researcher will benefit from a large variety of training available at CEA on HPC and computer programming as well as training at our industrial partners (ARM, Eviden) and Université Paris Saclay, which has MSc courses in radiobiology and also hosts a research centre (INanoTherad) dedicated to novel radiotherapy treatments, gathering physicists, radiobiologists and medical doctors. The activities will be carried out in the framework of the Marie Sklodowska Curie Action Doctoral Network EPACE (European compact accelerators, their applications, and entrepreneurship)