Design of a high-energy phase contrast radiography chain
As part of hydrodynamic experiments carried out at CEA-DAM, the laboratory is seeking, using pulsed X-ray imaging, to radiograph thick objects (several tens of mm), made of low-density materials (around 1 g/cm3), inside which shock waves propagate at very high speeds (several thousand m/s). For this type of application, it is necessary to use energetic X-ray sources (beyond 100 keV). Conventional X-ray imaging, which provides contrast due to variations in absorption cross sections, proves insufficient to capture the small density variations expected during the passage of the shock wave. A theoretical study recently carried out in the laboratory showed that the complementary exploitation of the information contained in the X-ray phase should enable better detectability. The aim of the post-doctorate is to provide experimental proof of concept for this theoretical study. For greater ease of implementation, the work will mainly focus on the dimensioning of a static X-ray chain, where the target is stationary and the source emits continuous X-ray radiation. Firstly, the candidate will have to characterize in detail the spectrum of the selected X-ray source as well as the response of the associated detector. In a second step, he (she) will design and have manufactured interference gratings adapted to high-energy phase measurements, as well as a representative model of the future moving objects to be characterized. Finally, the student will carry out radiographic measurements and compare them with predictive simulations. The student should have a good knowledge of radiation-matter interaction and/or physical and geometric optics. Proficiency in object-oriented programming and/or the Python and C++ languages would be a plus.
Lensfree Cytometry for High-troughput biological analysis
The new lensfree imaging is against the foot of the recent developments in microscopy that focuses today on super-resolution achievements. Instead lensfree imaging offers several advantages: field of view (FOV) can cover several cm2, resolution in the range of 0.5µm to 3µm, mostly compact sizes and ease of use. The technique is based on holography online as invented by Gabor [1]. A biological object is illuminated by a coherent light, micrometric structures of the object diffract and the light interferes with the incident wave. The amplitude of the interference is recorded by a CMOS sensor and the image is reconstructed thanks to inverse-problem approaches. Albeit the method exists since 1970, the recent development of large field, small pixel size digital sensors helped realize the full potential of this method only since 2010.
At CEA-LETI Health Division, a new microscopic platform based on this principle has been developed. Its applicability for performing high-throughput monitoring of major cell functions such as cell-substrate adhesion, cell spreading, cell division, cell division orientation, cell migration, cell differentiation, and cell death have been demonstrated [2,3]. The new project proposed in this PostDoc is dealing with the development of an innovative lensfree cytometry setup aiming at high-throughput analysis of biological samples, e.g. cell counting, cell sorting, etc. The post-doctoral fellow will develop the instrumentation and methods and will conduct the experimentation and analysis of true biological samples.
Modelling of interstitial cluster evolution in bcc metals after helium implantation
Under irradiation, structural materials inside nuclear reactors undergo changes in mechanical properties, which result from the formation of point defect clusters, such as cavities (clusters of vacancies) and interstitial dislocation loops (clusters of self-interstitial atoms). Understanding the formation processes of such clusters is thus of prime importance. Recently, three-dimensional interstitial clusters, known as C15 clusters, have been shown theoretically to be highly stable in iron [1]. In order to detect such clusters experimentally, an idea is to make them grow, as shown for dislocation loops after helium implantation [2].
This approach will be carried out experimentally in various bcc metals in the framework of the ANR project EPigRAPH, in collaboration with Chimie ParisTech, GEMaC and LPS.
In this project, the following modelling tasks will be performed by the postdoc:
- DFT calculations will be done to obtain the energetic properties of point defects and point defect clusters in the bcc metals envisaged in the project.
- These data will then be used to parameterize a kinetic model based on cluster dynamics [3]. This formalism is particularly well adapted to simulate the evolution of point defect clusters over long physical times.
The modelling work will be performed in close collaboration with another postdoc working on the experimental part.
[1] M. C. Marinica, F. Willaime, J.-P. Crocombette, Phys. Rev. Lett. 108 (2012) 025501
[2] S. Moll, T. Jourdan, H. Lefaix-Jeuland, Phys. Rev. Lett. 111 (2013) 015503
[3] T. Jourdan, G. Bencteux, G. Adjanor, J. Nucl. Mater. 444 (2014) 298
Simultaneous Localisation and Mapping with an RGB-D camera based on a direct and sparse method
Recent advances in the methods of locating a device (smartphone, robot) in relation
to its environment make it possible to consider the deployment of augmented reality solutions and autonomous robots. The interest of RGB-D cameras in such a context is notable since it allows to directly acquire the depth map of the perceived scene.
The objective of this post docorate consists in developping a new SLAM (Simultaneous Localisation and Mapping) method relying on a depth sensor.
To reach a solution both robust, accurate and with small CPU/memory comsumption, the depth image will be exploited though a direct and sparse approach. The resulting solution will be then combined with the solution of "RGB SLAM Constrained to a CAD model" developped in our laboratory, resulting finaly in an "RGB-D SLAM Constrained to a CAD model"
Study of the thermo-mechanical strains in the HEMT AlGaN/GaN on silicon
Fabricating the HEMT AlGaN/GaN device is complex and leads to the formation of crystalline defects. These strains, in the GaN layer, leads to crackings in the GaN layer or leads to a delamination at the top interface. Moreover, these mechanical strains conjugated to thermal strains during device working, can lead to a degradation of the electrical performance of the device.
This heterogeneous assembly, involve a complex behaviour. The various materials used, react differently to the thermal-mechanical strains. The requested work is to study and to model the distortion of this structure, in order to evaluate the strains effects on the electrical performance on lateral and vertical devices.
Automatic driving of a finite element software based upon a domain decomposition strategy. Application to ultrasonic non-destructive testing.
One the most important field of activity at the DISC (Department of Imaging and Simulation for Control) of CEA - LIST is to provide a comprehensive set of tools for modeling and simulation for Non-Destructive Testing (NDT). These tools are gathered within the computational platform CIVA. Most of the ultrasound models -- elaborated by the LSMA (research laboratory for Simulation and Modeling in Acoustics) -- are based upon semi-analytical methods. Although very efficient, these methods suffer from a loss of precision as soon as some critical phenomena (e.g. head waves or caustics) or some particular features of the material (e.g. flaws or heterogeneities ) appear in the control experiment. To circumvent these limitations, one of the field of research in the LSMA is to build coupling schemes between semi-analytical and numerical methods. Following this strategy, a computational software based upon high-order finite elements combined with domain decomposition strategies is developped in order to address 3D configurations. The work proposed here focuses on increasing the complexity of the configurations reachable within this coupling strategy. A typical example being the fluid-structure interaction in the case of flaws reaching the bottom of the material to control.
Scalable digital architecture for Qubits control in Quantum computer
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.
Application of formal methods for interferences management
Within a multidisciplinary technological research team of experts in SW/HW co-design tools by applying formal methods, you will be involved in a national research project aiming at developing an environment to identify, analyze and reduce the interferences generated by the concurrent execution of applications on a heterogeneous commercial-off-the-shelf (COTS) multi-core hardware platform.
Design of in-memory high-dimensional-computing system
Conventional von Neumann architecture faces many challenges in dealing with data-intensive artificial intelligence tasks efficiently due to huge amounts of data movement between physically separated data computing and storage units. Novel computing-in-memory (CIM) architecture implements data processing and storage in the same place, and thus can be much more energy-efficient than state-of-the-art von Neumann architecture. Compared with their counterparts, resistive random-access memory (RRAM)-based CIM systems could consume much less power and area when processing the same amount of data. This makes RRAM very attractive for both in-memory and neuromorphic computing applications.
In the field of machine learning, convolutional neural networks (CNN) are now widely used for artificial intelligence applications due to their significant performance. Nevertheless, for many tasks, machine learning requires large amounts of data and may be computationally very expensive and time consuming to train, with important issues (overfitting, exploding gradient and class imbalance). Among alternative brain-inspired computing paradigm, high-dimensional computing (HDC), based on random distributed representation, offers a promising way for learning tasks. Unlike conventional computing, HDC computes with (pseudo)-random hypervectors of D-dimension. This implies significant advantages: a simple algorithm with a well-defined set of arithmetic operations, with fast and single-pass learning that can benefit from a memory-centric architecture (highly energy-efficient and fast thanks to a high degree of parallelism).