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Field
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imaging facilities, such as one research-dedicated 3T scanner, MEG/EEG, TEP, TMS/tDCS and other useful resources. For more information, see: https://dreherteam.wixsite.com/neuroeconomics Where to apply
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, Engineering, or a related discipline, with strong programming experience (e.g., Python, JavaScript, MATLAB) and proven capability in developing interactive visualisations or interfaces. You will demonstrate
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agricultural datasets proficiency in R and/or Python for analysing spatial data, particularly in the context of remote sensing, ensuring robust data processing and interpretation strong ability to communicate
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academic progression More information: https://www.scilifelab.se/researchers/simon-koplev/ Qualifications Requirements A doctoral degree or an equivalent foreign degree. This eligibility requirement must be
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Biomarkers and Susceptibility Unit (UBS). To find out more, visit our website: https://www.odap-ico.org/ About the role We are seeking a highly motivated data scientist to join the research team on artificial
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simulations on the Aurora supercomputer, using AMReX (https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry-aware refinement strategies that go
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of the 2005 urban riots in France (https://doi.org/10.1038/s41598-017-18093-4 ). It will draw on data concerning these riots, the 2007 urban riots in the Ile-de-France region, and the Yellow Vests movement
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transportation research. Technical skills: Python programming; simulation. Domain knowledge: Decarbonization modeling, life-cycle assessment, battery-electric vehicle technologies; mining operations experience is
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.). Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow, PyTorch, or JAX. In-depth understanding of transformer architectures, attention mechanisms, and
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Python, R, or MATLAB. Solid understanding of battery systems, electrochemical processes, or energy management. Preferred Skills: Experience with time-series analysis, predictive modeling, and anomaly