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PostDoc
X-ray tomography reconstruction based on analytical methods and Deep-Learning
CEA-LIST develops the CIVA software platform, a reference for the simulation of non-destructive testing processes. In particular, it proposes tools for X-ray and tomographic inspection, which allow, for a given tomographic testing, to simulate all the radiographic projections (or sinogram) taking into account various associated physical phenomena, as well as the corresponding tomographic reconstruction. The...
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PostDoc
High entropy alloys determination (predictive thermodynamics and Machine learning) and their fast elaboration by Spark Plasma Sintering
The proposed work aims to create an integrated system combining a computational thermodynamic algorithm (CALPHAD-type (calculation of phase diagrams)) with a multi-objective algorithm (genetic, Gaussian or other) together with data mining techniques in order to select and optimize compositions of High entropy alloys in a 6-element system: Fe-Ni-Co-Cr-Al-Mo. Associated with computational methods, fast fabrication and...
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PostDoc
Development of Algorithms for the Detection and Quantification of Biomarkers from Voltammograms
The objective of the post-doctoral research is to develop a high-performance algorithmic and software solution for the detection and quantification of biomarkers of interest from voltammograms. These voltammograms are one-dimensional signals obtained from innovative electrochemical sensors. The study will be carried out in close collaboration with another laboratory at CEA-LIST, the LIST/DIN/SIMRI/LCIM, which will provide...
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PostDoc
Co-design strategy (SW/HW) to enable a structured spatio-temporal sparsity for NN inference/learning
The goal of the project is to identify, analyze and evaluate mechanisms for modulating the spatio-temporal sparsity of activation functions in order to minimize the computational load of transformer NN model (learning/inference). A combined approach with extreme quantization will also be considered. The aim is to jointly refine an innovative strategy to assess the impacts...
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PostDoc
Development of noise-based artifical intellgence approaches
Current approaches to AI are largely based on extensive vector-matrix multiplication. In this postdoctoral project we would like to pose the question, what comes next? Specifically we would like to study whether (stochastic) noise could be the computational primitive that the a new generation of AI is built upon. This question will be answered in...
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PostDoc
Causal learning
As part of a project that concerns the creation of innovative materials, we wish to strengthen our platform in its ability to learn from little experimental data. In particular, we wish to work firstly on the extraction of causal links between manufacturing parameters and properties. Causality extraction is a subject of great importance in AI...
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PostDoc
Quantum dot auto-tuning assisted by physics-informed neural networks
Quantum computers hold great promise for advancing science, technology, and society by solving problems beyond classical computersapos; capabilities. One of the most promising quantum bit (qubit) technologies are spin qubits, based on quantum dots (QDs) that leverage the great maturity and scalability of semiconductor technologies. However, scaling up the number of spin qubits requires overcoming...
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