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Applications are invited for a Research Associate/Fellow to join a research project in the Nottingham Nanoscience group (uonmmm.com) that focuses on imaging and spectroscopic probing of novel magnetic samples. This involves close collaboration between the Nanoscience and Spintronics groups in...
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work on a project to conduct research on learning from human demonstrations for robot manipulation used in household service and manufacturing. The roles of this position include: Develop robotic
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biology techniques, including DNA manipulation, PCR, and molecular cloning. Conduct protein expression, purification, and characterization using methods such as spectrophotometry, HPLC, and SDS-PAGE. Design
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blotting, histology, and genetic manipulation methods such as knockdown, knockout, and overexpression. Applicants should have a strong record of peer-reviewed publications and demonstrate excellent written
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that approximate natural settings, while allowing electrophysiological recordings, high-resolution optical imaging and optogenetic manipulation. In the current project, we are interested in studying learning and
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act robustly in complex real-world environments. The positions address complementary research challenges related to robotic perception, navigation, manipulation, and multi-robot cooperation across
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proficiency in Portuguese and English. Social skills and group work abilities are also mandatory. Applicants should have skills related to the set up and/or manipulation of databases and management of social
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, proven expertise in trypanosome genetic manipulation and functional analysis, as evidenced by first name authorship, proven expertise in the generation and analysis of proteomic and RNAseq data, as
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, proven expertise in trypanosome genetic manipulation and functional analysis, as evidenced by first name authorship, proven expertise in the generation and analysis of proteomic and RNAseq data, as
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augmentation, manipulation and dexterous manipulators, telepresence and teleoperation, industrial automation and robotics, active perception and learning, inspection robotics, hyper-redundant robots, robot