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to scholarly peer-reviewed publications. Opportunities exist for the selected applicant to mentor students and to develop learning opportunities (courses, workshops, etc.) for the UK earth science community. The
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about exploring and applying new statistical, computational, or machine learning techniques to astronomical data sets, and extending current methodology to be applicable in the era of big data. Looking
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oriented field relevant to the project, such as computer science, mathematics, statistics or physical sciences. Expertise in machine learning based and statistical data analysis, including the capability
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Seeking a Research Scientist or a Postdoctoral Researcher (25-1291)(Sequential Decision Making Team)
to sequential decision-making, such as online learning and reinforcement learning. The candidate is expected to carry out research and publish their findings in top machine-learning conferences, and help in
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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-contact manipulation/locomotion, machine learning and optimisation, avatar animation or related areas. You have experience working on real robots and great team working skills. Informal enquiries may be
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in the area of scientific computing and Computational Fluid Dynamics. Prior Experience in turbulence modelling, machine learning or the Lattice Boltzmann method is an advantage. Operational skills
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of Science at UvA. What are you going to do? The aim of the project is to use advanced Machine Learning techniques to predict the anharmonic vibrational spectra of large Polycyclic Aromatic Hydrocarbon (PAH
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Skills/Qualifications Strong background in Operating Systems and Linux development Knowledge of memory management mechanisms and system-level programming Experience with Machine Learning models (design
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growth for Portland, Maine, and northern New England. We are nurturing an environment for high-impact research and innovation in computer and data science, digital engineering, the advanced life sciences