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variational models and deep learning techniques. You will implement and validate reconstruction algorithms, ensuring their performance, robustness, and efficiency for clinical application. You will participate
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop
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-luminosity phase of the LHC. The successful candidate will work in close collaboration with other members of the Particle Physics team, and with members of the Computing, Algorithms and Data team at L2IT
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8 Oct 2025 Job Information Organisation/Company Universite de Montpellier Department Human Resources Research Field Mathematics Mathematics » Algorithms Mathematics » Applied mathematics Researcher
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experimental tools and protocols, including fluorescent reporters and genetic constructs, to monitor phenotypic variations in yeast in real time. The scientist will develop time-lapse microscopy, flow cytometry
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opportunity to choose between two missions: • Mission 1: Improve new automated algorithmic schemes to quickly, efficiently and robustly detect and extract recorded geophysical signals related to earthquakes
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of genetically modified mouse colonies carrying multiple transgenes - Selection of sampling methods for molecular, histological, and metabolomic analyses - Conducting analyses such as qPCR, histology, and
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algorithm development and satellite remote sensing • Good written and spoken English • Ability to work independently as well as in a team • Proficiency in programming languages (e.g. Python, R, Fortran
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Eligibility criteria . PhD in atmospheric sciences • Knowledge of cloud physics • Experience in algorithm development and satellite remote sensing • Good written and spoken English • Ability to work
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recognitions and multi-class neural network algorithms. We propose to apply this emerging method to study samples from Europe, South Africa, and East Asia dated between 1.8 Ma and 60 thousand years ago (ka