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EMBL International PhD Programme – 2026 Winter Recruitment Would you like to contribute your creativity to an international team of scientists from various disciplines focusing on basic research in
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that are transforming many sectors today through language models, recommendation systems and advanced technologies. However, modern machine learning models, such as neural networks and ensemble models, remain largely
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essential to potentially inhibit this helicase to reduce protein biosynthesis in cancer cells. The aim of the PhD project is to utilize small-angle neutron scattering to map the position of the N-terminal
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25 Sep 2025 Job Information Organisation/Company CNRS Department Institut de Chimie des Substances Naturelles Research Field Physics Researcher Profile First Stage Researcher (R1) Country France Application Deadline 15 Oct 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours...
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The PhD candidate will be hired by the CNRS and integrated
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Institut de chimie des milieux et matériaux de Poitiers - Equipe SAMCat | Poitiers, Poitou Charentes | France | about 1 month ago
science » Earth science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country France Application Deadline 26 Sep 2025 - 23:59 (Europe/Paris) Type of Contract Temporary Job Status
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dynamics in Energy Recovery Linacs (ERLs). ERLs are an emerging class of accelerators that combine the flexibility and high beam quality of linear accelerators with the efficiency of recirculating machines
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on food craving and health-related decision-making. To this purpose, we will use a combination of brain imaging, behavioral measures, and machine-learning techniques. Activities The successful candidate
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: electronic structure calculations (plane wave DFT if possible), statistical thermodynamics, molecular dynamics. Skills in Python, bash scripting, Fortran 90 and machine-learning would be appreciated. The PIIM
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) To develop Deep Learning algorithms to significantly speed up probabilistic inference algorithms of current spatial birth-death models 2) To incorporate fossil stratigraphic and spatial information into a new