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parameters affect material properties and functional performance, and interacting with machine-learning and modelling teams to translate experimental results into predictive datasets. Preparing reproducible
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We are looking for a Doctoral student to become part of our team at the Division of Applied Quantum Physics at the Department of Microtechnology and Nanoscience. Join our innovative theory team and
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with the application of Algorithms, Artificial intelligence (AI), and Machine Learning (ML) methods to accelerate material discovery and design, optimizing manufacturing processes in nanoscience
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Join us to pursue exciting research in a dynamic and out-of-the-box-thinking environment! About us The Department of Microtechnology and Nanoscience advances the frontiers in quantum technology
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to model them through computer simulations is highly valued. · Knowledge of classical molecular dynamics, including Machine Learning Interatomic Potentials. · Other research experience will be considered
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As a flagship research center in nanoscience and nanotechnology, our mission is to open and explore new frontiers of knowledge at the nanoscale, and bring value to society in the form of new
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to support machine learning model development to accelerate materials discovery: Perform high-throughput DFT and molecular dynamics simulations to investigate the thermodynamic, structural, and electronic
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, such as pulse design or numerical optimization Background in data-driven or machine-learning approaches relevant to optimal control (e.g., model learning, reinforcement learning) What you will do Take
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-volatile memory that is seen as a potential candidate for the replacement of Flash and SDRAM memory. However, it is their ability to emulate the memory and learning properties of biological synapses and
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Research Scientist to join Prof Molly Stevens’s lab at the Kavli Institute for Nanoscience Discovery. The ideal candidate will be a highly motivated postdoctoral researcher with strong expertise in organic