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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 11 hours ago
USNH Employees should apply within Workday through the Jobs Hub app The research position(s) are within the broad area of biophysics, structural modeling, and molecular dynamics (MD) simulations
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to inform and navigate future challenges. You will identify existing technologies which may be used to develop interactive simulations of evolving landscapes. These will be applied to available data sets
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tailoring their microstructure and to develop a direct link as well as a closed loop between processing and fabrication of materials on one hand, and material characterisation and simulation-theoretical
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controlled by tailoring their microstructure and to develop a direct link as well as a closed loop between processing and fabrication of materials on one hand, and material characterisation and simulation
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refine research directions. ● Perform simulation studies and real data analyses using high performance computing resources. ● Support for grant application. ● Prepare draft manuscripts, assist
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for the position (max. two pages) 3. Teaching experience 4. A certified/signed copy of a) PhD certificate and b) MSc certificate. 5. Publication list Application deadline: April 19, 2026, 23.59pm CET We reserve
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proposed molecular quantum computer architecture will be investigated for its use in real-world quantum computing and other applications, such as quantum simulations and precise quantum measurements
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in many aspects of the LZ experiment, from detector calibration, simulations to data analysis. The successful candidate will play an active role in analyzing LZ data, with opportunities to improve
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standard solar cell manufacturing processes. The project will also include optical simulation tasks and advanced luminescence characterization experiments. The postdoc will also contribute to the design of
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of the project is to design, model and simulate neural networks based on magnetic skyrmion nucleation and propagation. The second objective is to fabricate these hardware neural networks, characterize