Development of a Multilayer Encapsulation System for the Production of Core-Shell Microcapsules Suitable for Organoid Growth
Every year, 20 million people worldwide are diagnosed with cancer, with 9.7 million succumbing to the disease (Kocarnik et al., 2021). Personalized treatment could significantly reduce the number of deaths. This thesis addresses this challenge by proposing the development of organoids derived from patient biopsies to optimize treatments.
The bioproduction of encapsulated cells in biopolymers is a rapidly growing field, with applications in personalized medicine, research, drug screening, cell therapies, and bioengineering. This thesis aims to contribute to these fields by focusing on the multilayer encapsulation of cells in biopolymers with a wide range of viscosities.
The inner layer (core) provides an optimal environment for the maturation and survival of cells or organoids, while the outer layer (shell) ensures mechanical protection and acts as a filtering barrier against pathogens.
This new thesis aims to design, develop, and study—both analytically and numerically—the architecture of a dual-compartment nozzle for the high-frequency production of monodisperse core-shell capsules. It builds upon a previous thesis completed in 2023, which focused on the detailed characterization and development of a predictive model for the generation of single-layer microcapsules using centrifugal force alone.
The formation and ejection mechanisms of multilayer capsules are complex, involving the rheological properties of biopolymers, centrifugal force, surface tension, and interfacial dynamics. The nozzle architecture must account for these properties.
The first part of this thesis will focus on understanding the multilayer formation and ejection mechanisms of microcapsules as a function of nozzle geometry. This will allow the prediction and control of capsule formation based on the rheological properties of the biopolymers. The second part will involve developing an automated system for the aseptic production of capsules. Finally, biological validation will assess the functionality and reliability of the developed technology.
To achieve the objectives of this study, the candidate will first conduct analytical and numerical studies, design the ejection nozzles, and leverage the laboratory's expertise for their fabrication. Fluidic tests on prototypes will help optimize the design, leading to the development and testing of a fully operational microcapsule production system.
The ideal candidate will have a background in physics, engineering, and fluid mechanics, with a strong inclination for experimental approaches. Prior experience in microfluidics or biology would be a valuable asset.
All solid-state lithium batteries based on Pyrochlore solid electrolyte
Due to the increasing energy demand, developing efficient storage systems, both stationary and portable, is crucial. Among these, lithium-ion batteries stand out as the most advanced, capable of being manufactured using liquid or solid electrolytes. All-solid-state batteries have a bright future thanks to their non-flammable electrolytes and their ability to use metallic lithium to increase energy density. Although research on these batteries is dynamic, their commercialization is not yet a reality. Indeed, two significant obstacles to their development remain: the low intrinsic ionic conductivity of solids and the difficulty of obtaining good solid/solid interfaces within the composite electrodes and the complete system.
This thesis explores the potential of pyrochlore oxyfluoride as a new class of superionic material for all-solid-state batteries, which are more stable in air and have higher ionic conductivity than current solid oxide electrolytes. The electrochemical properties of all-solid-state batteries will be carefully examined using a combination of in situ and operando techniques, such as XRD, Raman, ion beam/synchrotron analysis, solid-state NMR, X-ray tomography, etc.
Keywords :
Solid electrolyte, All-solid battery, Nuclear magnetic resonance, Electrochemistry, Pyrochlore Oxyfluoride, in situ/operando, Spectroscopy, Synchrotron
Simplified modelling of calcination in a rotating tube
As part of the reprocessing of uranium oxide spent fuel, the final high-level liquid waste is packaged in glass using a two-stage process, calcination followed by vitrification. Calcination gradually transforms the liquid waste into a dry residue, which is mixed with preformed glass in a melting furnace. The calciner consists of a rotating tube heated by a resistance furnace. The calcined solutions consist of nitric acid and compounds in their nitrate form or insolubles in the form of metal alloys. In order to improve control of the calciner, it is proposed to model it.
The modelling will consist of creating and then coupling three models:
- A thermodynamic model to represent the transformations undergone by the material. This part will almost certainly involve ATD and ATG measurements, coupled with a design of experiments type approach (1st year).
- A material flow model. The literature already contains very simplified principles for representing the flow in a rotating tube calciner, but we will have to be innovative, in particular by defining tests to characterise the flow of the material during the calcination process (2nd year).
A thermal model that will take into account exchanges between the furnace and the calciner tube as well as exchanges between the material and the tube. The exchange coefficients will have to be characterised (1st year).
Combining these three models (3rd year) will give rise to an initial simplified calcination model. This model will be used to help control the calcination stage and also to train operators to control this apparatus.
You will be working in the LDPV, a multidisciplinary team (process, chemistry, fluid mechanics, modelling, mechanics, induction) comprising 16 engineers and technicians. A team with 30 years' experience in vitrification processes, recognised both nationally and internationally.
Development of thin film negative electrodes for Li-free all-solid-state batteries
The aim of this work is to develop 'Li-free' negative electrodes for new generations of high energy density all-solid-state lithium batteries. The function of this type of electrode is to provide a significant gain in energy density in the battery, to facilitate its manufacture by eliminating the need to handle lithium metal and, most importantly, to enable the formation of a homogeneous, dendrite-free lithium film when the battery is charged.
These electrodes will be based on the functionalisation of a metal collector with thin-film materials comprising at least one lithiophilic material (typically a compound that can be alloyed with lithium) and an inorganic ionic conductor. These electrodes are prepared by physical vacuum deposition processes such as sputtering or thermal evaporation. It will therefore be necessary to study the influence of the composition and structure of the lithiophilic layer on the nucleation and growth mechanism of the lithium film and on the evolution of the electrode during charge/discharge cycles. The role of chemical/mechanical interactions with the ionic conducting layer will also be investigated.
This work, which is part of a national CEA/CNRS joint project, will be carried out at the CEA Tech site in Pessac, which has a full range of vacuum deposition and thin film characterisation equipment, in close collaboration with ICMCB CNRS in Bordeaux. It will benefit from the many characterisation resources (confocal optical microscopy, SEM/cryo FIB, ToF-SIMS, SS-NMR, µ-XRD, AFM,...) available in the various partner laboratories involved in the project.
Sub-10nm CMOS performances assessment by co-optimization of lithography and design
While developing and introducing new technologies (ex. FDSOI 10nm CMOS), design rules (DRM) are the guidelines used to ensure that a chip design can be reliably fabricated. These rules govern the physical dimensions and spacing of various features used by the designer in the chip layout. They translate both device electrical constraints and manufacturing processes constraints. Among them, lithography and patterning processes are critical step in defining the intricate structures and features on a semiconductor wafer. The most efficient design rules can only be obtained from a co-optimization merging design and lithography constraints.
The objective of this research work is to demonstrate that the use of a digital lithography twin can improve the performance of CMOS by co-optimization of design and lithography (DTCO).
Starting from specific use cases for FDSOI 10nm CMOS technologies, and using advanced lithography simulation tools, the candidate would :
- Develop novel characterization methods to assess lithography process capabilities (hotspot prediction).
- Assess design rules with respect to the lithography process capabilities.
- Quantify, though design rules, lithography impact on device performances.
- Identify significant both process and design limitations and propose paths to challenge them.
As PhD student of CEA-Leti, you will join a technology research institute dedicated to micro and nanotechnologies, within a dynamic and international research environment. You will join the Computational Patterning Laboratory with strong connections with integrated circuit design experts of CEA-Leti. You will benefit from the exceptional facilities of the institute's 300mm clean room and from state-of-the art lithography software tools.
You will be encouraged to publish your work and participate to international conferences and workshops.
CCA-secure constructions for FHE
Fully Homomorphic Encryption (FHE) is a corpus of cryptographic techniques that allow to compute directly over encrypted data. Since its inception around 15 years ago, FHE has been the subject of a lot of research towards more efficiency and better practicality. From a security perspective, however, FHE still raises a number of questions and challenges. In particular, all the FHE used in practice, mainly BFV, BGV, CKKS and TFHE, achieve only CPA-security, which is sometimes referred to as security against passive adversaries.
Over the last few years, a number of works have investigated the security of FHE in the beyond-CPA regime with new security notions (CPAD, FuncCPA, vCCA, vCCAD, and others) being proposed and studied, leading to new attacks and constructions and, overall, a better understanding of FHE security in that regime.
With respect to CCA security, recent works (2024) have defined new security notions, which are stronger than CCA1 and shown to be achievable by both exact and approximate FHE schemes. Leveraging on these advances, the present thesis will aim to design practical FHE-style malleable schemes enforcing CCA security properties, at least for specific applications.
Water at the hydrophilic direct bonding interface
The microelectronics industry is making increasing use of hydrophilic direct bonding technology to produce innovative substrates and components. CEA LETI's teams have been leaders in this field for over 20 years, offering scientific and technological studies on the subject.
The key role of water at the bonding interface can be newly understood thanks to a characterization technique developed at CEA LETI. The aim of this thesis is to confirm or refute the physico-chemical mechanisms at play at the bonding interface, depending on the surface preparations and materials in contact.
A large part of this work will be carried out on our cleanroom tools. The characterization of surface hydration using this original technique will be complemented by standard characterizations such as adhesion and adherence energy measurements, FTIR-MIR and SIMS analyses, and X-ray reflectivity at ESRF.
Scalable NoC-based Programmable Cluster Architecture for future AI applications
Context
Artificial Intelligence (AI) has emerged as a major field impacting various sectors, including healthcare, automotive, robotics, and more. Hardware architectures must now meet increasingly demanding requirements in terms of computational power, low latency, and flexibility. Network-on-Chip (NoC) technology is a key enabler in addressing these challenges, providing efficient and scalable interconnections within multiprocessor systems. However, despite its benefits, designing NoCs poses significant challenges, particularly in optimizing latency, energy consumption, and scalability.
Programmable cluster architectures hold great promise for AI as they enable resource adaptation to meet the specific needs of deep learning algorithms and other compute-intensive AI applications. By combining the modularity of clusters with the advantages of NoCs, it becomes possible to design systems capable of handling ever-increasing AI workloads while ensuring maximum energy efficiency and flexibility.
Summary of the Thesis Topic
This PhD project aims to design a scalable, programmable cluster architecture based on a Network-on-Chip tailored for future AI applications. The primary objective will be to design and optimize a NoC architecture capable of meeting the high demands of AI applications in terms of intensive computing and efficient data transfer between processing clusters.
The research will focus on the following key areas:
1. NoC Architecture Design: Developing a scalable and programmable NoC to effectively connect various AI processing clusters.
2. Performance and Energy Efficiency Optimization: Defining mechanisms to optimize system latency and energy consumption based on the nature of AI workloads.
3. Cluster Flexibility and Programmability: Proposing a modular and programmable architecture that dynamically allocates resources based on the specific needs of each AI application.
4. Experimental Evaluation: Implementing and testing prototypes of the proposed architecture to validate its performance on real-world use cases, such as image classification, object detection, and real-time data processing.
The outcomes of this research may contribute to the development of cutting-edge embedded systems and AI solutions optimized for the next generation of AI applications and algorithms.
The work performed during this thesis will be presented at international conferences and scientific journals. Certain results may be patented.
Development of multiplexed photon sources for quantum technologies
Quantum information technologies offers several promises in domains such as computation or secured communications. There is a wide variety of technologies available, including photonic qubits. The latter are robust against decoherence and are particularly interesting for quantum communications applications, even at room temperature. They also offers an alternative to other qubits technologies for quantum computing. For the large-scale deployment of those applications, it is necessary to have cheap, compact and scalable devices. To reach this goal, silicon photonics platform is attractive. It allows implementing key components such as generation, manipulation and detection of photonic qubits.
Solid-state photon generation may occur with different physical processes. Among those, the non-linear photon pair generation has several benefits, such as working at room temperature, the ability to generate heralded single photon, or entangled photon pairs…
You will work on multiplexed parametric photon pair sources in order to surpass the inherent limits of the physical process for generating photon pairs. This will include the development, the fabrication monitoring, and the characterization in the laboratory. In the goal of a full integration on chip, it is necessary to be able to filter effectively unwanted light, in order to keep only photons of interest.
Hardware-aware Optimizations for Efficient Generative AI with Mamba Networks
Generative AI has the potential to transform various industries. However, current state-of-the-art models like transformers face significant challenges in computational and memory efficiency, especially when deployed on resource-constrained hardware. This PhD research aims to address these limitations by optimizing Mamba networks for hardware-aware applications. Mamba networks offer a promising alternative by reducing the quadratic complexity of self-attention mechanisms through innovative architectural choices. By leveraging techniques such as sparse attention patterns and efficient parameter sharing, Mamba networks can generate high-quality data with significantly lower resource demands. The research will focus on implementing hardware-aware optimizations to enhance the efficiency of Mamba networks, making them suitable for real-time applications and edge devices. This includes optimizing training and inference times, as well as exploring potential hardware accelerations. The goal is to advance the practical deployment of generative AI in resource-constrained domains, contributing to its broader adoption and impact.