Machine learning technics and knowledge-based simulator combined for dynamic process state estimation
This project aims to estimate the real state of a dynamic process for liquid-liquid extraction through the real data record. Data of this kind are uncertain due to exogenous variables. They are not included inside the simulator PAREX+ dedicated to the dynamic process. So, the first part of the project is to collect data from simulator. By this way the operational domain should be well covered and the dynamic response recorded. Then, the project focuses to solve the inverse problem by using convolutionnal neural networks on times series. Maybe a data enrichment could be necessary to perfect zones and improve estimations. Finally, the CNN will be tested on real data and integrate the uncertainty inside its estimations.
At the end, the model built needs to be used in operational conditions to help diagnosis and improve the real-time control to ensure that the dynamic observed is the one needed.
Development of processing by Artificial Intelligence of a measuring and forecasting station
This post-doctoral proposal is part of the French atomic commission (CEA) project "MultiMod'Air", which involves developing an « intelligent » prototype of air quality measurement and forecasting station within two years. The work proposal is to develop the bricks of Artificial Intelligence (AI) of the project: correction by ANN (Artificial Neuronal network) of the measurements obtained through low cost sensors, correction ANN of weather forecasts at the station level, which are simple treatments to implement. The actual research work will concern the development of a AI based pollution forecast at the station by learning from past events.
DTCO analysis of MRAM for In/Near-Memory Computing
The energy cost associated to moving data across the memory hierarchy has become a limiting factor in modern computing systems. To mitigate this trend, novel computing architectures favoring a more local and parallel processing of the stored information are proposed, under the labels « Near/In-Memory Computing » or « Processing In Memory ». Substantial benefits are expected in particular for computationally complex (e.g. combinatorial optimization, graph analysis, cryptography) and data-intensive tasks (e.g. video stream analysis, bio-informatics). Such applications are especially demanding in terms of endurance, latency and density. SRAM, fulfilling the first two criteria, may eventually suffer from its footprint and static power consumption. This prompts the evaluation of alternative denser and non-volatile memory technologies, with magnetoresistive memories (MRAM) currently leading in terms of speed-endurance trade-off.
The primary objective will be to estimate improvements brought by MRAM in terms of array-level power, performance, area (PPA), as compared to SRAM-based on-chip memories and for advanced technology nodes. The candidate will establish an analysis and benchmarking workflow for various classes of MRAM, and optimize single bit cells based on a compact model for the memory element. This baseline approach will then be adapted to functional variations specific to IMC in order to assess the benefits of MRAM on an integrated test vehicle.
Combinatorial optimization of base materials for the design of new materials
The design of new materials is a field of growing interest, especially with the emergence of additive manufacturing processes, thin film deposition, etc. In order to create new materials to target properties of interest for an application area, it is often necessary to mix several raw materials.
A physicochemical modeling of the reactions that occur during this mixing is often very difficult to obtain, especially when the number of raw materials increases. We want to free ourselves as much as possible from this modeling. From experimental data and business knowledge, the goal of this project is to create a symbolic AI capable of groping for the optimal mixture to achieve one or more given properties. The idea is to adapt existing methods of operations research, such as combinatorial optimization, in a context of imprecise knowledge.
We will focus on different use cases such as electric batteries, solvents for photovoltaic cells and anti-corrosion materials.
Within the project, you will:
• Study the state of the art,
• Propose one or several algorithms to prototype, and their evaluation,
• Disseminate the resulting innovations to the consortium and the scientific community, through presentations, contributions to technical reports and / or scientific publications.
Maximum duration: 18-24 months (regarding your experience).
Modelling and evaluation of the future e-CO2 refinery
In the context of achieving carbon neutrality by 2050, the CEA has initiated a project in 2021 to assess the relevance of coupling a nuclear power system with a direct atmospheric carbon capture device (DAC) thanks to the use of the system's waste heat.
As a member of a team of about twenty experts(energy system evaluation, techno-economic engineering, energy system modeling, optimization and computer programming), you will participate in a research project on the modeling and evaluation of a CO2 refinery dedicated to the production of Jet Fuel fed by a nuclear reactor and coupled with an atmospheric CO2 capture process.
Design of Ising Machines based on a network of spintronics oscillators copled through CMOS circuitry
Our information and communication society is asking for always more computing tasks of increasing complexity. Their energy bargain increases quickly so that it is mandatory to find new architecture of computing processors with improved energy efficiency.
The post doc applicant will contribute to the design of Ising machines which are computing architectures inspired from biology and physics and which permit to solve complex optimization problems. Under the scope of SpinIM project (french ANR funding), the applicant will contribute to the demonstration of an Ising machine based on the electrical coupling of spin torque nano-oscillators (STNO). More specifically, the post doc role will be to design the configurable CMOS chip implementing the electrical coupling. He will have to propose a VerilogA model of the STNO with the help of Spintec experience on STNO theory. Then the post doc will have to propose an optimized design of the CMOS chip from schematics to layout and he will have to assess the chip performances in laboratory. Finally, the post doc will participate to the demonstration of the full Ising machine consisting of the CMOS chip and a STNO network on some optimization tasks. The post doc will take place in the LGECA laboratory of CEA-Leti which have gained experience on CMOS-Spintronics co-design.
Photonic Accelerators: Driving Innovation in Quantum Simulations
Photonic circuits, specialised low-power processors, are emerging as one of the most promising technologies for accelerating the execution of complex algorithms in the fields of machine learning and scientific computing, while maintaining low heat dissipation.
The success of simulating quantum systems and implementing quantum-inspired simulation algorithms on photonic units suggests the potential of these accelerators to advance computing capabilities in the fields of computational chemistry and materials science.
The aim of this project is to integrate photonic technologies with neural and tensor networks, pushing back the limits of quantum simulations and classical devices. This is a promising direction for the future of hardware-accelerated, specialised algorithmic innovation.
This research will focus on adapting algorithms to photonic devices, optimising energy consumption and developing new algorithms inspired by the specificities of hardware.
Optimization of Li metal/electrolyte for the next generation of all-solid-state battery
CEA Tech Nouvelle-Aquitaine, created in 2013, set up a new laboratory, since more than two years, focused on both the development of materials and the high throughput screening to accelerate the discovery of materials for the next generations of Li-ion batteries. For that, the CEA Tech Nouvelle-Aquitaine acquires different vacuum deposition equipment (sputtering, evaporation, atomic layer deposition) integrated in glovebox and different automated characterization techniques (SEM-EDX, profilometer, XRD, LIBS and confocal microscope later).
The Li metal/electrolyte interface constitutes one of the main challenges to overcome for the next generation of all-solid-state battery. The reactions of decompositions at the interface associated to uneven plating/stripping of Li ions lead to quick cell failure. One of the avenue for stabilizing it is to use a protective layer, which must feature numerous physical-chemical properties. In this context, this internal CEA project aims at setting up a combinatorial synthesis methodology associated to high throughput characterizations in order to accelerate the discovery of new protective layers at the Li metal/electrolyte interface.
We are seeking for an outstanding applicant who will be in charge of setting up the whole methodology, from the synthesis to the physical-chemical-electrochemical characterizations of the materials. She/he will have at her disposal a new state-of-the-art infrastructures. She/he will collaborate with other CEA labs located at LITEN (Grenoble, France).
Attack detection in the electrical grid distributed control
To enable the emergence of flexible and resilient energy networks, we need to find solutions to the challenges facing these networks, in particular digitization and the protection of data flows that this will entail, and cybersecurity issues.
In the Tasting project, and in collaboration with RTE, the French electricity transmission network operator, your role will be to analyze data protection for all parties involved. The aim is to verify security properties on data in distributed systems, taking into account that those induce a number of uncertainties.
To this end, you will develop a tool-based methodology for protecting the data of power grid stakeholders. The approach will be based on formal methods, in particular runtime verification, applied to a distributed control system.
This postdoc position is part of the TASTING project, which aims to meet the key challenges of modernizing and securing power systems. This 4-year project, which started in 2023, addresses axis 3 of the PEPR TASE call “Technological solutions for the digitization of intelligent energy systems”, co-piloted by CEA and CNRS, which aims to generate innovations in the fields of solar energy, photovoltaics, floating wind power and for the emergence of flexible and resilient energy networks. The targeted scientific challenges concern the ICT infrastructure, considered as a key element and solution provider for the profound transformations that our energy infrastructures will undergo in the decades to come.
The project involves two national research organizations, INRIA and CEA through its technological research institute CEA-List. Also involved are 7 academic laboratories: G2Elab, GeePs, IRIT, L2EP, L2S and SATIE, as well as an industrial partner, RTE, which is supplying various use cases.
ACCELERATING a DSN SWEEP KERNEL ALGORITHM FOR NEUTRONICS BY PORTING ON GPU.
In the framework of the Programmes Transversaux de Compétences (PTC or literally Cross-XXX Programme), the DES/ISAS/DM2S/SERMA/LLPR and the CEA-DIF are both working on the porting of deterministic neutron transport codes on GPU.
The DM2S within the Energies Direction (DES) is responsible for research and development activities on the numerical methods and codes for reactor physics, amongst which the APOLLO3® code. The neutronics laboratory of CEA-DIF is responsible for developing tools for deterministic methods in neutronics for the Simulation programme.
These two laboratories are actively preparing for the advent of new generation of supercomputers where GPU (Graphical Processing Units) will be predominant. Indeed, the underlying numerical problems to be solved along with the working methodology as well as the conclusions and experience which will be obtained from such studies may be rationalised between both laboratories. Thus, this work has given rise to this postdoctoral position which will be common to both teams. The postdoctoral researcher will be formally based at SERMA at CEA Saclay, with nevertheless regular meetings with the CEA-DIF scientists.
The postdoctoral research work is to study the acceleration of a toy model of a 3D discrete ordinates diamond-differencing sweep kernel (DSN) by porting the code on GPU. This work hinges on porting experiments which have previously been carried by both teams following two different approaches: a ‘’high-level’’ one based on the Kokkos framework for DES and a ‘’low-level’’ approach based on Cuda for CEA-DIF.