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Field
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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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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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of the areas listed on the page above are of interest to you. The list includes positions covering Operator Algebras, Machine Learning, Analytic Number Theory, Automorphic Forms and Representation Theory
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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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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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machine learning, computer vision, human-computer interaction, or similar relevant areas. Experience in research or development on bias, interpretability, and/or privacy in machine learning/AI is necessary
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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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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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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