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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 hours ago
development and lifelong learning and enjoy exclusive perks for numerous retail, restaurant and performing arts discounts, savings on local child care centers and special rates on select campus events. UNC
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assistant is a fixed-term scientific position of 6 months. If you have hands-on experience with analytical chemistry and laboratory techniques and experience with troubleshooting, optimization of laboratory
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main focus on the development of control software. ▪ You will design and implement advanced control and readout protocols and optimize experimental characterization workflow,s leveraging machine learning
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system analysis or comparable Initial experience in optimization or statistics is an advantage Initial experience of an object-oriented programming language (e.g. Python, Matlab) and MS Office Analytical
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promotion and for the compatibility of family and career; numerous opportunities for further training, including language courses, IT courses and other courses. TUD strives to employ more women in academia
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Apply Job Type Full-time Description Job ID AF9515 -0617-1850 Position Classification FT Administrator Position Summary This position is designed to provide PhD-level expertise in helping numerous
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, especially in ultracold quantum gases or condensed matter theory Proven analytical, computational, and modelling skills Experience with numerical simulations of quantum or many-body systems A deep curiosity
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optimise a ‘Digital Twin’ of the Tees estuary to ensure that the NBS are deployed at locations optimal for performance and longevity while operating within the constraints placed upon deployment by other
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, operation and optimization of fs to ps laser sources Characterization of electrostatic potentials and excited carrier concentrations using off-axis electron holography Mapping of charge carrier lifetimes by
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the potential to accelerate materials design and optimization. By leveraging large datasets and complex algorithms, ML models can uncover intricate relationships between composition, processing parameters, and