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offering a postdoctoral position in multi-messenger astrophysics related to the KM3NeT, SVOM, and COLIBRI detectors. The main objective is to complete the development and optimization of the KM3NeT neutrino
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microstructure of bone tissue. This PhD project will investigate lattice structures designed to achieve optimal dynamic performance, as for instance advanced dampers to mitigate unwanted vibrations, or high
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” focusing on the effect of a fluctuating environment on the collective dynamics of self-propelled agents, a numerical part on “reinforcement learning” focusing on optimizing communication between agents in a
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to efficiently identify materials with optimal mechanical properties and controlled degradability. The primary task is to develop techniques for synthesizing degradable polymers and copolymers using ring-opening
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research contractors. Through the design and characterization of new materials, the laboratory's approach leads to the optimization of a wide range of properties for targeted applications. The position is in
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metrological performance of the ASIC/detector system. Optimizing the signal-to-noise ratio (SNR) is therefore a key focus of the study, particularly as it must be achieved in conjunction with optimizing speed
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optimizations are still needed to adapt the translation of these mRNAs to the cell types of interest. As part of a collaboration with Chantal Pichon's team (University of Orleans), this project aims to use
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activities, as well as the development and/or validation of the performance of various hydrogeological and hydrological calculation codes. You will bring innovative approaches to optimize and anticipate
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of unlearning in artificial systems. Unlearning is crucial for biological organisms to adapt and remain flexible in dynamic environments, as well as for machines to optimize output integrity by shedding outdated
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a family of specific peptide substrates for the PKs of interest (PKs of the JAK, GRK, IRAK, RIPK and ERK/MAPK families) (2) developing and optimizing fluorescent peptide biosensors from the identified