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learning as well as a strong background in scientific programming (in languages like Julia, Python, Fortran or C/C++). The applicant must hold a PhD in physical oceanography, atmospheric sciences, computer
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strategies within it. You will combine archaeological and landscape data (maps, satellite imagery, archaeological datasets) with machine learning approaches to build a system that highlights promising
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reinforcement learning. The project will investigate a simulation platform that reproduces the structure of real prospection activities by integrating multiple, heterogeneous geospatial and archaeological data
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criteria We seek a researcher with strong machine learning modelling expertise with experience in the analysis of challenging large-scale data sets. Experience with cellular imaging data or virology
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generally, the project is part of a large initiative at Serval and SnT, which aims to support the reliable deployment of machine learning systems by providing industry actors with practical evaluation tools
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technologies including semantic/task-oriented data processing, signal processing, and network resource management to improve the performance of future wireless communication systems. Finally, due to the large