Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

See all positions [+]

Development of cellulose-based materials for the conception of biomedical devices by stereolithograpy

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire de Formulation des Matériaux

01-11-2020

SL-DRT-20-0628

sebastien.rolere@cea.fr

Health and environment technologies, medical devices (.pdf)

The development of innovative medical devices mainly relies on the use of high performance multifunctional materials. These materials should display high biocompatibility and controlled degradability, and advantageously specific biological properties, such as muco-adhesion, antimicrobial features, or bioaffinity. Such advanced materials are keys for biomedical research activities. Additive manufacturing technologies are particularly well-suited for the technical specifications of biomedical devices. Notably, StereoLithography Apparatus (SLA) allows the processing of complex geometries from UV-light curing of liquid materials. SLA is currently under consideration to develop biomedical devices from cellulose materials. Cellulose is a biocompatible bio-based polymer, extracted from renewable resources. Cellulose chemical structure possesses many hydroxyl functional groups for potential chemical modification and further biomolecules attachment. The aim of the present project is the design and fabrication, using SLA, of biomedical devices able to present various bio-specific properties, from chemically-modified cellulose materials.

Download the offer (.zip)

Multi-scale modeling of the electromagnetic quantum dot environment

Département Composants Silicium (LETI)

Laboratoire de Simulation et Modélisation

01-10-2020

SL-DRT-20-0637

helene.jacquinot@cea.fr

New computing paradigms, circuits and technologies, incl. quantum (.pdf)

Multi-scale modeling of the electromagnetic quantum dot environment In the near future, emerging quantum information technologies are expected to lead to breakthroughs in the world of high performance computing and secure communication. Among solid-state approaches, ?Silicon on Insulator? (SOI) based spin quantum bit (qubit) is an alternative approach to nowadays superconducting based one [1]. They are much more compact and have demonstrated over the last few years significant achievements, with long coherence time and fast single qubit rotation. A clear challenge is now to investigate the scalability issues going from single to multiple of the spin qubits in SOI, taking into account its associated classical CMOS platform used for control, read-out and initialization of the quantum state [2]. The main goal of this PhD work is to assess different strategies to implement spin control on 2D qubit arrays using microwaves signals. The candidate will i) characterize radio-frequency (RF) test structures at very low temperature using state-of-the-art equipment and compare results with dedicated electromagnetic simulations, ii) develop a toolbox to allow multi-scale optimization from single to qubit arrays, iii) integrate RF spin microwave control for 2D qubit array using CEA-LETI silicon technologies. This PhD work will be performed in the frame of a tripartite collaborative project between CEA-LETI, CEA-IRIG and CNRS-Institut Néel (ERC ?Qucube?). [1] Maurand, R. et al. A CMOS silicon spin qubit, Nat. Communications 7, 13575 (2016). [2] Meunier, T. et al. Towards scalable quantum computing based on silicon spin, Symp. on VLSI Technology, 2019.

Download the offer (.zip)

Embedding of high temperature resistant Fiber Bragg Gratings into metal structures obtained by additive manufacturing processes

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs Fibres Optiques

01-10-2020

SL-DRT-20-0645

guillaume.laffont@cea.fr

Additive manufacturing, new routes for saving materials (.pdf)

LCFO laboratory from the Technological Research Division at CEA List, in partnership with the LISL laboratory from the CEA DEN, specialized in metal additive layer manufacturing processes, proposes a PhD thesis aiming at developing methods to integrate optical fiber sensors (OFS) based on high temperature resistant Fiber Bragg Gratings (FBGs) in metallic components obtained thanks to metal additive layer manufacturing processes either for the aerospace or for the nuclear industry. Thanks to recent developments, ultra-stable FBGs have been realized using direct writing processes into silica optical fibers with femtosecond lasers. These temperature and strain transducers combined with special optical fibers designed for very high temperature environments will be considered for the instrumentation of components obtained by metal additive layer manufacturing. This project aims at contributing to the adoption of in situ monitoring of 3D-printed metallic components, paving the way for their Structural Health Monitoring (SHM) to anticipate failures in the fabrication process and to optimize operating costs thanks to the development of predictive and conditional maintenance-based procedures.

Download the offer (.zip)

Federated learning: collaboration vs. personalization

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Science des Données et de la Décision

01-09-2020

SL-DRT-20-0661

aurelien.mayoue@cea.fr

Artificial intelligence & Data intelligence (.pdf)

In 2016, Google introduced the founding principles of federated learning [1] which opened up a brand new computing paradigm for AI. Until now, most of deep machine learning approaches adopt a centralized way to train a model. It requires the data to be stored in a datacenter. This is practically what giant AI companies have been doing over the years. However, this centralized approach is privacy-intrusive as users of the service have to send their data to the service provider which manages the datacenter. Federated learning is a collaborative process which leaves the training data distributed on the client devices and learns a shared model by aggregating locally-computed updates. As the data remains in its original location, the privacy is improved and the cost communication also decreases. The independent and identically distributed (IID) sampling of the training data is a key point to train a machine learning model. It ensures that the stochastic gradient is an unbiased estimate of the full gradient. But, in a decentralized learning process, it is unrealistic to assume that the local data on each edge device is always IID. ?Non-iidness? can be the cause of a significant decrease of the model accuracy. To improve the performance of federated learning whatever the local distribution of data, we investigate a way of personalizing the models at edge which permits each node to fine-tune its model locally while continuing to train the shared model. [1] Google AI blog: https://ai.googleblog.com/2017/04/federated-learning-collaborative.html

Download the offer (.zip)

Robust and distributed multi-agent-system-based data-stream learning in a collaborative environment

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Intelligence Artificielle et Apprentissage Automatique

01-09-2019

SL-DRT-20-0665

sandra.garciarodriguez@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Nowadays, data streams are present in more and more applications and domains where dynamism and speed truly matter. Research challenges open lines about the generation and processing of these streams, especially in distributed, heterogeneous and collaborative environments. Existing ones lack, in general, the means of collaborating, negotiating, sharing, or validating data streams on such kind of heterogeneous environments. Multi-agent systems principles enable some of these features. However there is still work to do in order to comply them with the characteristics of data streams. The main line of this proposal is using distributed agents in data streams to deal with different challenges as managing non-synchronised streams from different sources, increasing the robustness of online models that deal with such streams (to make them reliable through unexpected environment changes) and generating new metrics to evaluate the needs mentioned before. This work would rely in the "Streamer" framework already existing in the laboratory.

Download the offer (.zip)

Resonators and devices based on elastic waves obtained through the hybridization of surface and bulk waves

Département Composants Silicium (LETI)

Laboratoire Composants Radiofréquences

01-09-2020

SL-DRT-20-0668

alexandre.reinhardt@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

Bulk or surface elastic wave devices are currently an enabling technology for radiofrequency emission/reception circuits used in mobile phones. Since, at constant frequency, the wavelength of elastic wave is close to 100,000 times smaller than electromagnetic wavelengths, the treatment of a signal carried by elastic waves instead of an electrical signal offers a tremendous miniaturization. With the increase in frequency bands operated simultaneously by each single mobile phone, requirements brought onto radiofrequency filters become more and more stringent. This motivates the research on new types of components exploiting new elastic waves. Conventional technologies rely on bulk acoustic waves (BAW) or surface acoustic waves (SAW) propagating respectively along the thickness or the surface of a piezoelectric material. Such kind of materials offer the possibility to couple electric signals into elastic waves, and conversely. In the last few years, a new kind of propagation mode, called "hybrid SAW/BAW" has been proposed, based on the excitation of waves by a periodic array of piezoelectric stubs. First realizations have been proposed, but their properties are not yet fully determined. This PhD subject focuses therefore on the study of the potentialities offered by these new kinds of modes. On one hand, the properties of such waves are strongly related to the combination of piezolectric material, of the nature of the substrate, on their respective crystal orientations as well as on the geometric dimensions of the piezoelectric stubs. The candidate will therefore investigate the design space in order to reveal what performances can be expected from such structures and optimise their design towards applications such as RF filters or time references, ideally for applications above 3 GHz. This work will leverage the simulation models available at CEA-LETI and those developped by the FrecNSys company. A second part of the PhD is expected also to explore more fundamental possibilites opened by these modes arising from the coupling between elastic surface waves and a periodic array of electrically active structures. Such periodic structures belong to the broader range of so-called elastic metamaterials, which offer unusual propagation properties such as frequency ranges in which wave propagation is forbidden, artificial slowing of waves, strong confinement or nonreciprocal propagation. Since active structures are involved, additional interesting effects may be explored. The candidate will leverage the expertise on elastic metamaterials brought by the acoustic department of ISEN. Eventually, an experimental part will be devoted to the proposition of designs to be implemented in the clean rooms of CEA-LETI and participation to the technological developments. The goal is here to assess the exprimental feasibility of such structures.

Download the offer (.zip)

174 Results found (Page 7 of 29)
first   previous  5 - 6 - 7 - 8 - 9  next   last

See all positions