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auto-encoder, back-propagation, • knowledge of R (main programming language), Python and C++. Application Application files should contain a resumé, an application letter and grade records of the 2 last
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, clustering, classification • deep learning, variational auto-encoder, back-propagation, • knowledge of R (main programming language), Python and C++. Application: Application files should contain a resumé, an
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on optimization) and in general be keen on using mathematics to model real problems and get insights. He should also be knowledgeable on machine learning and have good programming skills. Previous experiences with
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methods, clustering, and machine learning. Advanced level of expertise in the R programming language Skills appreciated: Mastery of classical tools and methods for analyzing NGS (Next Generation Sequencing
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-quality training and learning materials, and a sustainable plan. FSTM's main tasks are to contribute to the definition of the body of knowledge and competencies, assist in developing the skill tree used as
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experimental data and is testable across multiple unlearning scenarios. For this we plan to apply for the first time Spiking Neural Networks (SNNs) to the modeling of unlearning. SNNs have recently shown
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datasets [10, 11]. It will also be tested on the autonomous vehicles of the ACENTAURI team. 3 Work Plan The work of this postdoc includes: - Studying the state-of-the-art of LiDAR-camera fusion and VLM
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) based on various libraries. It is developed in modern C++ and offers C, Fortran, and Python application programming interfaces. PDI offers a reference system similar to Python or C++’s shared_ptr with