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Post-doctoral position in AI safety and assurance at CEA LIST

Département Ingénierie Logiciels et Systèmes (LIST)

Labo.conception des systèmes embarqués et autonomes

01-10-2020

PsD-DRT-20-0110

morayo.adedjouma@cea.fr

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.

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New Al(Ga)N templates for optimized epitaxial growth of UV LEDs

Département des Plateformes Technologiques (LETI)

Laboratoire

01-01-2021

PsD-DRT-20-0113

guillaume.rodriguez@cea.fr

UV-C LEDS are to replace Hg based UV-lamps for applications related to air and water sterilization and decontamination , a subject of importance in the present day health context. However, UV-C LEDs, which rely on the use of AlGaN nitride alloy epitaxial layers, are still not efficient enough to be considered as a possible alternative on a large industrial scale. This is so for different reasons, related to the quality of the nitride material and/or to the technology employed for fabricating the diodes. We have created an academic consortium which, around LETI, can address the different issues at stake, in order to develop a true technology stream in this field. One of the issue concerns the structural quality of the epitaxial layers, dislocations being non-radiative defects that kill the internal efficiency of the LED. Within the consortium, we are in charge of developping new and optimized template layers, i.e. the epitaxial layer nucleated on the surface of the sapphire substrate, since an important part of the defects in the LEDs arise from this nucleation layer. To address this point, we will develop new and original methods for the deposition of AlN and AlGaN templates: reactive sputtering, co-sputtering (for alloy templates) and Pulsed laser Deposition (PLD). The layers will be characterized on the Nano-Characterization Platfom of LETI and will be evaluated according to the electro-optical properties of the LEDs structures that will be grown by epitaxy on top of these templates.

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Approximate Bayesian inference based on stochastic simulation

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Modélisation et Simulation des Systèmes

01-12-2020

PsD-DRT-20-0114

eric.barat@cea.fr

In many scientific fields, from particle physics to cosmology, including molecular biology and epidemiology, it is now common practice to develop simulation tools in order to describe complex phenomena. These simulation-based models are often stochastic (Monte Carlo) and have multiple input parameters. While the primary object of stochastic simulation is to be able to generate data from a configuration of parameters (forward simulation), its practical interest often resides in the opposite problem: determining a configuration of parameters of the model making it possible to generate data sufficiently close to those observed in Nature. Knowledge of these parameters can then represent the objective of the study or be used to calibrate the simulator for subsequent analyzes. However, solving such a nonlinear and very indirect problem is in general a difficult task. Our goal is to build a rigorous statistical inference framework for estimating these parameters. In particular, we propose to adopt the Bayesian paradigm for the resolution of the inverse problem in order to characterize the set of solutions via their a posteriori distribution. However, this objective comes up against a fundamental difficulty here: we do not have the analytical expression of likelihood in the context of stochastic simulation (likelihood free). This challenge has recently appeared to be amenable thanks to the emergence of two complementary techniques: ABC (Approximate Bayesian Computation) and deep generative models. As part of this project, we propose to evaluate the feasibility of this approach in an application scenario in the field of stochastic particle transport.

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Scalable digital architecture for Qubits control in Quantum computer

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Intégration Silicium des Architectures Numériques

01-01-2021

PsD-DRT-20-0116

eric.guthmuller@cea.fr

Scaling Quantum Processing Units (QPU) to hundreds of Qubits leads to profound changes in the Qubits matrix control: this control will be split between its cryogenic part and its room temperature counterpart outside the cryostat. Multiple constraints coming from the cryostat (thermal or mechanical constraints for example) or coming from Qubits properties (number of Qubits, topology, fidelity, etc?) can affect architectural choices. Examples of these choices include Qubits control (digital/analog), instruction set, measurement storage, operation parallelism or communication between the different accelerator parts for example. This postdoctoral research will focused on defining a mid- (100 to 1,000 Qubits) and long-term (more than 10,000 Qubits) architecture of Qubits control at room temperature by starting from existing QPU middlewares (IBM QISKIT for example) and by taking into account specific constraints of the QPU developed at CEA-Leti using solid-state Qubits.

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Fast-scintillator-based device for on-line FLASH-beam dosimetry

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs et Architectures Electroniques

01-12-2020

PsD-DRT-20-0127

dominique.tromson@cea.fr

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.

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