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Field
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highly motivated Doctoral Research Fellow in a full time (100 %) fixed term position available for three (3) years at the Faculty of Computer Science, Engineering and Economics, Department
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) in an appropriate discipline. Subject Area Computer Science & IT, Electrical & Electronic Keywords Artificial intelligence, floating-point arithmetic, numerical analysis, computer arithmetic, machine
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teaching or other departmental duties, up to a maximum of 20 per cent of full-time. Your qualifications You have graduated at a Master’s level in Computer Science, Mathematics, or a closely related subject
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real-time during behavioural tasks Integrating virtual reality paradigms with behaviour and imaging to assess decision-making processes Exploring potential therapeutic interventions for NMDA receptor
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molecular biology, quantitative imaging and biophysical approaches to investigate cell shape changes in cultured cells and in vivo. Current projects in the lab include investigating the regulation
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degree or MSc degree (Distinction/Merit) in engineering, computer science, or other closely related fields. Experience with ROS and proficient programming language skills (C++, Python, MATLAB) would be
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, performing data collection and processing of cryo-EM data. Setup of future hardware and software as the research technology is upgraded. Providing lab members support in data collection, image processing, map
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 5 hours ago
opportunities for career growth, skill development and lifelong learning and enjoy exclusive perks that include numerous retail and restaurant discounts, savings on local child care centers and special rates
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(bio-)chemistry, physics, and engineering expertise to study molecules and cells, taking advantage of optical and single-molecule imaging, molecular probes, molecular biology, and 'large' data analysis
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to the fields of synthetic biology, bio-derived materials, and bioinspired design. The outcomes of this research will provide a foundation for understanding and replicating natural mineralisation processes