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the nanofabrication of microdevices (using techniques such as thin film deposition, electron-beam lithography and etching), and their electrical transport characterization. A major objective is to explore
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meriting to have one or more of the following skills: General skills in the manufacture and characterization of thin-film samples, e.g. spin coating, physical vapor deposition, spray coating and surface
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the synthesis of novel 2D materials (e.g., thin-film deposition by PVD and CVD). Proven programming skills (e.g., Python) for instrument control and data analysis. You are a highly motivated and independent
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at the Department of Physics are materials physics, nuclear physics and particle physics. In materials physics, the core research fields are the physics of nanomaterials and thin films, quantum matter and quantum
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of nanomaterials and thin films, quantum matter and quantum technologies, light-matter interaction and physics and chemistry of nanoparticles. Nuclear physics concentrates on nuclear astrophysics, nuclear structure
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physics and particle physics. In materials physics, the core research fields are the physics of nanomaterials and thin films, quantum matter and quantum technologies, light-matter interaction and physics
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at the Department of Physics are materials physics, nuclear physics and particle physics. In materials physics, the core research fields are the physics of nanomaterials and thin films, quantum matter and quantum
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, the core research fields are the physics of nanomaterials and thin films, quantum matter and quantum technologies, light-matter interaction and physics and chemistry of nanoparticles. Nuclear physics
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printing, PVD thin films) Collaborate with industrial and academic partners and contribute to joint publication Qualifications To be considered for the position, you must have: Strong experience with
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collections. The challenges of these collections offer a unique opportunity to take AI to the next level. Future AI technologies should be applicable outside laboratories and be able to learn from sparse