Fast-scintillator-based device for on-line FLASH-beam dosimetry
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.
Post-doctoral position in AI safety and assurance at CEA LIST
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.
Developement of relaxed pseudo-substrate based on InGaN porosified by electrochemical anodisation
As part of the Carnot PIRLE project starting in early 2021, we are looking for a candidate for a post-doctoral position of 24 months (12 months renewable) with a specialty in material science. The project consists in developing a relaxed pseudo-substrate based on III-N materials for µLEDs applications, especially for emission in red wavelength. The work will focus on developing an InGaN-based epitaxy MOCVD growth process, on an innovative substrate based on electrochemically anodized and relaxed materials. He (She) will have characterize both the level of relaxation of the re-epitaxied layer and its crystalline quality. These two points will promote the epitaxial regrowth of an effective red LED. The candidate will be part of the team, working on the PIRLE project, will be associated to the work on red LED growth and its optical and electro-optical characterizations.
High precision robotic manipulation with reinforcement learning and Sim2Real
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.
Formalization of the area of responsibility of the actors of the electricity market
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.
Non-volatile asynchronous magnetic SRAM design
In the applicative context of sensor nodes as in Internet of things (IoT) and for Cyber Physical Systems (CPS), normally-off systems are mainly in a sleeping state while waiting events such as timer alarms, sensor threshold crossing, RF or also energetic environment variations to wake up. To reduce power consumption or due to missing energy, the system may power off most of its components while sleeping. To maintain coherent information in memory, we aim at developing an embedded non-volatile memory component. Magnetic technologies are promising candidates to reach both low power consumption and high speed. Moreover, due to transient behavior, switching from sleeping to running state back and forth, asynchronous logic is a natural candidate for digital logic implementation. The position is thus targeting the design of an asynchronous magnetic SRAM in a 28nm technology process. The memory component will be developed down to layout view in order to precisely characterize power and timing performances and allow integration with an asynchronous processor. Designing such a component beyond current state of the art will allow substantial breakthrough in the field of autonomous systems.
Measurement of active cell nematics by lensless microscopy
At CEA-Leti we have validated a video-lens-free microscopy platform by performing thousands of hours of real-time imaging observing varied cell types and culture conditions (e.g.: primary cells, human stem cells, fibroblasts, endothelial cells, epithelial cells, 2D/3D cell culture, etc.). And we have developed different algorithms to study major cell functions, i.e. cell adhesion and spreading, cell division, cell division orientation, and cell death.
The research project of the post-doc is to extend the analysis of the datasets produced by lens-free video microscopy. The post-doc will assist our partner in conducting the experimentations and will develop the necessary algorithms to reconstruct the images of the cell culture in different conditions. In particular, we will challenge the holographic reconstruction algorithms with the possibility to quantify the optical path difference (i.e. the refractive index multiplied by the thickness). Existing algorithms allow to quantify isolated cells. They will be further developed and assessed to quantify the formation of cell stacking in all three dimensions. These algorithms will have no Z-sectioning ability as e.g. confocal microscopy, only the optical path thickness will be measured.
We are looking people who have completed a PhD in image processing and/or deep learning with skills in the field of microscopy applied to biology.
Digital circuit design for In-Memory Computing in advanced Resistive-RAM NVM technology
For integrated circuits to be able to leverage the future “data deluge” coming from the cloud and cyber-physical systems, the historical scaling of Complementary-Metal-Oxide-Semiconductor (CMOS) devices is no longer the corner stone. At system-level, computing performance is now strongly power-limited and the main part of this power budget is consumed by data transfers between logic and memory circuit blocks in widespread Von-Neumann design architectures. An emerging computing paradigm solution overcoming this “memory wall” consists in processing the information in-situ, owing to In-Memory-Computing (IMC).
CEA-Leti launched a project on this topic, leveraging three key enabling technologies, under development at CEA-Leti: non-volatile resistive memory (RRAM), new energy-efficient nanowire transistors and 3D-monolithic integration [ArXiv 2012.00061]. A 3D In-Memory-Computing accelerator circuit will be designed, manufactured and measured, targeting a 20x reduction in (Energy x Delay) Product vs. Von-Neumann systems.
Application of a MDE approach to AI-based planning for robotic and autonomous systems
The complexity of robotics and autonomous systems (RAS) can only be managed with well-designed software architectures and integrated tool chains that support the entire development process. Model-driven engineering (MDE) is an approach that allows RAS developers to shift their focus from implementation to the domain knowledge space and to promote efficiency, flexibility and separation of concerns for different development stakeholders. One key goal of MDE approaches is to be integrated with available development infrastructures from the RAS community, such as ROS middleware, ROSPlan for task planning, BehaviorTree.CPP for execution and monitoring of robotics tasks and Gazebo for simulation.
The goal of this post-doc is to investigate and develop modular, compositional and predictable software architectures and interoperable design tools based on models, rather than code-centric approaches. The work must be performed in the context of European projects such as RobMoSys (www.robmosys.eu), and other initiatives on AI-based task planning and task execution for robotics and autonomous systems. The main industrial goal is to simplify the effort of RAS engineers and thus allowing the development of more advanced, more complex autonomous systems at an affordable cost. In order to do so, the postdoctoral fellow will contribute to set-up and consolidate a vibrant ecosystem, tool-chain and community that will provide and integrate model-based design, planning and simulation, safety assessment and formal validation and verification capabilities.
Simulation and electrical characterization of an innovative logic/memory CUBE for In-Memory-Computing
For integrated circuits to be able to leverage the future “data deluge” coming from the cloud and cyber-physical systems, the historical scaling of Complementary-Metal-Oxide-Semiconductor (CMOS) devices is no longer the corner stone. At system-level, computing performance is now strongly power-limited and the main part of this power budget is consumed by data transfers between logic and memory circuit blocks in widespread Von-Neumann design architectures. An emerging computing paradigm solution overcoming this “memory wall” consists in processing the information in-situ, owing to In-Memory-Computing (IMC).
However, today’s existing memory technologies are ineffective to In-Memory compute billions of data items. Things will change with the emergence of three key enabling technologies, under development at CEA-LETI: non-volatile resistive memory, new energy-efficient nanowire transistors and 3D-monolithic integration. At LETI, we will leverage the aforementioned emerging technologies towards a functionality-enhanced system with a tight entangling of logic and memory.
The post-doc will perform electrical characterizations of CMOS transistors and Resistive RAMs in order to calibrate models and run TCAD/spice simulations to drive the technology developments and enable the circuit designs.