Development of noise-based artifical intellgence approaches
Current approaches to AI are largely based on extensive vector-matrix multiplication. In this postdoctoral project we would like to pose the question, what comes next? Specifically we would like to study whether (stochastic) noise could be the computational primitive that the a new generation of AI is built upon. This question will be answered in two steps. First, we will explore theories regarding the computational role of microscopic and system-level noise in neuroscience as well as how noise is increasingly leveraged in machine leaning and artificial intelligence. We aim to establish concrete links between these two fields and, in particular, we will explore the relationship between noise and uncertainty quantification.
Building on this, the postdoctoral researcher will then develop new models that leverage noise to carry out cognitive tasks, of which uncertainty is an intrinsic component. This will not only serve as an AI approach, but should also serve as a computational tool to study cognition in humans and also as a model for specific brain areas known to participate in different aspects of cognition, from perception to learning to decision making and uncertainty quantification.
Perspectives of the postdoctoral project should inform how future fMRI imaging and invasive and non-invasive electrophysiological recordings may be used to test theories of this model. Additionally, the candidate will be expected to interact with other activates in the CEA related to the development of noise-based analogue AI accelerators.
LLMs hybridation for requirements engineering
Developing physical or digital systems is a complex process involving both technical and human challenges. The first step is to give shape to ideas by drafting specifications for the system to be. Usually written in natural language by business analysts, these documents are the cornerstones that bind all stakeholders together for the duration of the project, making it easier to share and understand what needs to be done. Requirements engineering proposes various techniques (reviews, modeling, formalization, etc.) to regulate this process and improve the quality (consistency, completeness, etc.) of the produced requirements, with the aim of detecting and correcting defects even before the system is implemented.
In the field of requirements engineering, the recent arrival of very large model neural networks (LLMs) has the potential to be a "game changer" [4]. We propose to support the work of the functional analyst with a tool that facilitates and makes reliable the writing of the requirements corpus. The tool will make use of a conversational agent of the transformer/LLM type (such as ChatGPT or Lama) combined with rigorous analysis and assistance methods. It will propose options for rewriting requirements in a format compatible with INCOSE or EARS standards, analyze the results produced by the LLM, and provide a requirements quality audit.
Development of piezoelectric resonators for power conversion
CEA-Leti has been working to improve energy conversion technologies for over 10 years. Our research focuses on designing more efficient and compact converters by leveraging GaN-based transistors, thereby setting new standards in terms of ultra-fast switching and energy loss reduction.
In the pursuit of continuous innovation, we are exploring innovative paths, including the integration of piezoelectric mechanical resonators. These emerging devices, capable of storing energy in the form of mechanical deformations, offer a promising perspective for increased energy density, particularly at high frequencies (>1 MHz). However, the presence of parasitic resonance modes impacts the overall efficiency of the system. Therefore, we are in need of an individual with skills in mechanics, especially in vibrational mechanics, to enhance these cleanroom-manufactured micromechanical resonators.
You will be welcomed in Grenoble within a team of engineers, researchers and doctoral students, dedicated to innovation for energy, which combines the skills of microelectronics and power systems from two CEA institutes, LETI and LITEN, close to the needs of the industry (http://www.leti-cea.fr/cea-tech/leti/Pages/recherche-appliquee/plateformes/electronique-puissance.aspx).
If you are a scientifically inclined mind, eager to tackle complex challenges, passionate about seeking innovative solutions, and ready to contribute at the forefront of technology, this position/project represents a unique opportunity. Join our team to help us push the boundaries of energy conversion.
References : http://scholar.google.fr/citations?hl=fr&user=s3xrrcgAAAAJ&view_op=list_works&sortby=pubdate
Modeling of charge noise in spin qubits
Thanks to strong partnerships between several research institutes, Grenoble is a pioneer in the development of future technologies based on spin qubits using manufacturing processes identical to those used in the silicon microelectronics industry. The spin of a qubit is often manipulated with alternating electrical (AC) signals through various spin-orbit coupling (SOC) mechanisms that couple it to electric fields. This also makes it sensitive to fluctuations in the qubit's electrical environment, which can lead to large qubit-to-qubit variability and charge noise. The charge noise in the spin qubit devices potentially comes from charging/discharging events within amorphous and defective materials (SiO2, Si3N4, etc.) and device interfaces. The objective of this postdoc is to improve the understanding of charge noise in spin qubit devices through simulations at different scales. This research work will be carried out using an ab initio type method and also through the use of the TB_Sim code, developed within the CEA-IRIG institute. This last one is able of describing very realistic qubit structures using strong atomic and multi-band k.p binding models.
Calibration of the high dose rate flash therapy beam monitor of the IRAMIS facility
Ultra-flash beams are pulsed beams of high-energy electrons (over a hundred MeV) with pulse durations in the femto-second range. The IRAMIS facility (CEA Saclay) uses laser acceleration to produce this type of beam, with a view to their application in radiotherapy. The LNHB is in charge of establishing dosimetric traceability for the IRAMIS facility, and to do this it has to calibrate the facility's monitor. Current radiotherapy facilities are based on medical linear accelerators operating at energies of up to 18 MeV in electron mode. LNHB has such equipment. It is used to establish national references in terms of absorbed dose to water, under the conditions of the IAEA protocol TRS 398.
Establishing dosimetric traceability involves choosing the measurement conditions, knowing the transfer dosimeter characteristics used and any corrections to be applied to the measurements taking into account the differences between the IRAMIS Facility and those of LNHB.
Optimization of a metrological approach to radionuclide identification based on spectral unmixing
The Laboratoire national Henri Becquerel (LNE-LNHB) at CEA/Saclay is the laboratory responsible for French references in the field of ionizing radiations. For several years now, it has been involved in the development of an automatic analysis tool for low-statistics gamma spectra, based on the spectral unmixing technique. This approach makes it possible to respond to metrological constraints such as robust decision-making and unbiased estimation of counts associated with identified radionuclides. To extend this technique to field measurements, and in particular to the deformation of spectra due to interactions in the environment of a radioactive source, a hybrid spectral unmixing model combining statistical and automatic learning methods is currently being developed. The aim of this mathematical solution is to implement a joint estimation of the spectra measured and the counts associated with the radionuclides identified. The next step will be to quantify the uncertainties of the quantities estimated from the hybrid model. The aim is also to investigate the technique of spectral unmixing in the case of neutron detection with a NaIL detector. The future candidate will contribute to these various studies in collaboration with the Laboratoire d'ingénierie logicielle pour les applications scientifiques (CEA/DRF).
Postdoc in Multi-instrumented operando monitoring of Li-ion battery for ageing
Nowadays, the development of new battery technology requires increasing the knowledge of degradation mechanisms occur inside the cell and monitor the key parameter in real time during cycling to increase the performances, lifetime and safety of the cells. To achieve these goals development of new sensing technology and integration inside and outside the cell is needed. The goal of the SENSIGA project is used advanced sensing technology to improve the monitoring of the cell by acquiring useful data correlate to the degradation process and develop more efficient battery management system with accurate state estimators. SENSIGA is a part of PEPR Batteries lead by CNRS and CEA and funding by the French Research Programme FRANCE 2030 to accelerate the development of new battery technology.
You will have the opportunity to work in a stimulating scientific environment focusing on the characterisation of both state of the art and latest generations of battery materials. Based on the sensing technology developed at CEA and from the state of the art, the SENSIGA project will reach the objective of the BATTERY2030+ roadmap goals for smart cells (https://battery2030.eu/research/roadmap/). One of the objectives of the project is to use external sensors to monitor the key parameters of the cell related to performances, ageing and safety behaviours.
Development of Algorithms for the Detection and Quantification of Biomarkers from Voltammograms
The objective of the post-doctoral research is to develop a high-performance algorithmic and software solution for the detection and quantification of biomarkers of interest from voltammograms. These voltammograms are one-dimensional signals obtained from innovative electrochemical sensors. The study will be carried out in close collaboration with another laboratory at CEA-LIST, the LIST/DIN/SIMRI/LCIM, which will provide dedicated and innovative electrochemical sensors, as well as with the start-up USENSE, which is developing a medical device for measuring multiple biomarkers in urine.
X-ray tomography reconstruction based on analytical methods and Deep-Learning
CEA-LIST develops the CIVA software platform, a reference for the simulation of non-destructive testing processes. In particular, it proposes tools for X-ray and tomographic inspection, which allow, for a given tomographic testing, to simulate all the radiographic projections (or sinogram) taking into account various associated physical phenomena, as well as the corresponding tomographic reconstruction.
The proposed work is part of the laboratory's contribution to a European project on tomographic testing of freight containers with inspection systems using high-energy sources. The spatial constraints of the projection acquisition stage (the trucks carrying the containers pass through an inspection gantry) imply an adaptation of the geometry of the source/detector system and consequently of the corresponding reconstruction algorithm. Moreover, the system can only generate a reduced number of projections, which makes the problem ill-posed in the context of inversion.
The expected contributions concern two distinct aspects of the reconstruction methodology from the acquired data. On the one hand, it is a question of adapting the analytical reconstruction methods to the specific acquisition geometry of this project, and on the other hand, to work on methods allowing to overcome the lack of information related to the limited number of radiographic projections. In this objective, supervised learning methods, more specifically by Deep-Learning, will be used both to complete the sinogram, and to reduce the reconstruction artifacts caused by the small number of projections available. A constraint of adequacy to the data and the acquisition system will also be introduced in order to generate physically coherent projections.
Development of a new spectrometer for the characterization of the radionuclide-based neutron sources
Since few years, the LNHB is developing a new instrument dedicated to the neutron spectrometry, called AQUASPEC. The experimental device consists of a polyethylene container that is equipped with a central channel accommodating the source and 12-measurement channels (in a spiral formation) around the source, into which detectors can be placed. The container is filled with water in order to moderate neutrons emitted from the source. Measurements have performed with 6Li-doped plastic scintillators, optimized for the simultaneous detection of fast neutrons, thermal neutrons and gamma rays through the signal processing based on pulse shape discrimination (PSD). The spectrum reconstruction is performed with an iterative ML-EM or MAP-EM algorithm, by unfolding experimental data through the detector's responses matrix calculated with MCNP6 code. The candidate will work in the general way on issues related to the neutron spectrometry in the laboratory: Contribution to the development and validation of the new spectrometer AQUASPEC; Participation to the sources measurements and working on aspects of neutron detection and signal processing, in particular issue of the discrimination of neutron/gamma based on the pulse shape discrimination technique (PSD); Usage of Monte Carlo simulation codes and algorithms to reconstruct initial neutron energy distribution; Investigation and integration of information related to neutron/gamma coincidence specific to the XBe type sources.