In order to replace current high environmental impact construction materials, CEA leads research work on chemical functionalization of wood (from French local forests) to improve its properties and make them a viable substitute of these construction materials or imported construction wood.
In this frame, chemistry under supercritical CO2 appears to be an efficient way to carry innovative chemistries while liùmiting the environmental impact & VOCs emissions of such processes.
Thus, you will be in charge of the development of new processes of chemical modification of local wood species under supercritical CO2. You will lead the research project by perfroming the state of the art, making technical propositions (around the adapted functionalization chemistries), carrying out the eperiments & the characterizations and will be in charge of respecting the deadlines & redacting the associated deliverables.
The LNE PLATINUM project (PLATFORM OF MODULAR NUMERICAL INSTRUMENTATION) aims to develop a modular platform, in order to test new instrumentation using two or more detectors in coincidence. The principle implemented in this project is based on the simultaneous detection of interactions taking place in two different detectors, by collecting information on the type of particle and its energy (spectroscopy). This principle is the basis for absolute measurements of activity or active continuous background reduction systems to improve detection limits. But it also allows the measurement of parameters characterizing the decay scheme, such as internal conversion coefficients, X-ray fluorescence yields or angular correlations between photons emitted in cascade.
Thanks to its expertise in atomic and nuclear data, the LNHB has noted for many years the incompleteness of decay schemes for certain radionuclides. These schemes, established at the time of evaluation from existing measured data, sometimes present inconsistencies or poorly known transitions, in particular in the presence of highly converted gamma transitions or very low intensity (for example, recent studies on 103Pa, 129I and 147Nd have revealed such inconsistencies). It therefore appears important for LNHB to better master the technique of coincidence measurement, taking advantage of the new possibilities in terms of data acquisition and time stamping to provide additional information on decay scheme and contribute to their improvement.
First, the post-doc candidate will have to integrate the solver Library Alien into Helix to carry out performance and usability assessments in iterative or direct solver configuration. These assessments will be done on different computer architecture from desktop computer to national supercomputer with thousands of cores.
In a second time, the candidate will deal with the possibility to add new functionalities in the Alien library to solve non-linear systems composed with equations and inequations to be able to solve, in an HPC context, mechanical problems like phase field problem or contact problems, problems often still opened in the community. The results will be compared to the classical test cases and benchmarks of the state of the art in the domain.
The candidate will join the Helix development team, formed by 3/4 developers for the moment in the laboratory LM2S (15 persons). A transversal program between CEA directions finances the post-doc and the candidate will collaborate with the Alien library developers at the DAM of CEA.
In the near future, emerging quantum information technologies are expected to lead to global breakthroughs in high performance computing and secure communication. Among semiconductor approaches, silicon-based spin quantum bits (qubits) are promising thanks to their compactness featuring long coherence time, high fidelity and fast qubit rotation [Maurand2016], [Meunier2019]. A main challenge is now to achieve individual qubit control inside qubit arrays.
Qubit array constitutes a compact open system, where each qubit cannot be considered as isolated since it depends on the neighboring qubit placement, their interconnection network and the back-end-line stack. The main goal of this post-doctoral position is to develop various implementation of spin control on 2D qubit array using multi-scale electromagnetic (EM) simulation ranging from nanometric single qubit up to millimetric interconnect network.
The candidate will i) characterize radio-frequency (RF) test structures at cryogenic temperature using state-of-the-art equipment and compare results with dedicated EM simulations, ii) evaluate the efficiency of spin control and allow multi-scale optimization from single to qubit arrays [Niquet2020], iii) integrate RF spin microwave control for 2D qubit array using CEA-LETI silicon technologies.
The candidate need to have a good RF and microelectronic background and experience in EM simulation, and/or design of RF test structures and RF characterization. This work takes place in a dynamic tripartite collaborative project between CEA-LETI, CEA-IRIG and CNRS-Institut Néel (ERC “Qucube”).
To date seizure suppression stimulation technologies (electrical stimulation) are majorly based on seizure detection procedure. No study has provided sound evidence that prospective seizure prediction/forecasting can be used to trigger closed loop therapeutics for drug resistant epilepsy treatment. Our proposal is based on the existing motor brain-computer interface algorithms already in clinical use. They can be adapted to generate prediction/forecasting of seizures occurrence. Our working hypothesis is that treating during high-risk seizures periods and not during the actual seizure would require relatively minor doses of the therapeutical element. This will reduce the power consumption and open the door to fully implantable system. Decoding algorithms will be potentially redesigned to respond better to the epileptic seizures forecasting task. They will be compared to the state of the art CNN based approaches, and other approaches. Prediction/forecasting seizures algorithms will be evaluated in an epilepsy model established at Clinatec, using non-human primates, and the algorithms will be refined over time. Cooling the epileptic foci is an effective way to stop de seizure before generalization. This model allows us to test the efficacy of the algorithms in treating focal seizures. An assessment of hardware embedding design constraints would be conducted to facilitate next steps for the clinical device development. The project will benefit from a collaboration between Clinatec and DSYS/SSCE; and will be in line with upcoming activities of LETI’s artificial intelligence platform.
New cancer treatment modalities aim to improve the dose delivered to the tumor while sparing healthy tissue as much as possible. Various approaches are being developed, including the temporal optimization of the dose delivered with very high dose rate irradiation (FLASH).
In this particular case, recent studies have shown that FLASH irradiation with electrons was as effective as photon beam treatments for tumor destruction while being less harmful to healthy tissue. For these beams, the instantaneous doses are up to several orders of magnitude higher than those produced by conventional beams. Conventional active dosimeters saturate under irradiation conditions at very high dose rates per pulse, therefore on-line dosimetry of the beam is not possible.
We propose to develop a dosimeter dedicated to the measurement of beams in FLASH radiotherapy based on an ultra-fast plastic scintillator coupled with a silicon photomultiplier sensor (SiPM). The novelty of the project lies both in the chemical composition of the plastic scintillator which will be chosen for its response time and its wavelength emission to have a response adapted to the impulse characteristics of the beam, and in the final sensor with the possibility of coupling the plastic scintillator to a miniaturized SiPM matrix.
The final goal is to be able to access, with a reliable methodology, the dosimetry and in-line geometry of FLASH beams.
The position is related to safety assessment and assurance of AI (Artificial Intelligence)-based systems that used machine-learning components during operation time for performing autonomy functions. Currently, for non-AI system, the safety is assessed prior to the system deployment and the safety assessment results are compiled into a safety case that remains valid through system life. For novel systems integrating AI components, particularly the self-learners systems, such engineering and assurance approach are not applicable as the system can exhibit new behavior in front of unknown situations during operation.
The goal of the postdoc will be to define an engineering approach to perform accurate safety assessment of AI systems. A second objective is to define assurance case artefacts (claims, evidences, etc.) to obtain & preserve justified confidence in the safety of the system through its lifetime, particularly for AI system with operational learning. The approach will be implemented in an open-source framework that it will be evaluated on industry-relevant applications.
The position holder will join a research and development team in a highly stimulating environment with unique opportunities to develop a strong technical and research portfolio. He will be required to collaborate with LSEA academic & industry partners, to contribute and manage national & EU projects, to prepare and submit scientific material for publication, to provide guidance to PhD students.
High precision robotic assembly that handles high product variability is a key part of an agile and a flexible manufacturing automation system. To date however, most of the existing systems are difficult to scale with product variability since they need precise models of the environment dynamics in order to be efficient. This information is not always easy to get.
Reinforcement learning based methods can be of interest in this situation. They do not rely on the environment dynamics and only need sample data from the system to learn a new manipulation skill. The main caveat is the efficiency of the data generation process.
In this post-doc, we propose to investigate the use of reinforcement learning based algorithms to solve high precision robotic assembly tasks. To handle the problem of sample generation we leverage the use of simulators and adopt a sim2real approach. The goal is to build a system than can solve tasks such as those proposed in the World Robot Challenge and tasks that the CEA’s industrial partners will provide.
The CEA is currently developing a simulation tool which models the energy exchanges between the actors of the electricity market but which models, in addition, the exchanges of information between those actors. The first results of this work show that, for some new energy exchange schemes, ’indirect’ interactions between actors may appear and may cause financial damage (for example, the failure of a source of production of one actor may impact the income of another). Thus, the borders which clearly delimited until now the areas of responsibility of each actors could be brought to blur and their areas of responsibility could "overla". The candidate will be responsible for:
- Formally define the area of responsibility of an actor in the electricity market,
- Model the interactions, including ’indirect’ ones, that may appear between these actors,
- Apply formal proof techniques (such as ’model-checking’) to detect overlaps in areas of responsibility,
- Define the conditions of exchange between the actors which would guarantee the non-recovery of the areas of responsibility.