22 computational-physics-"https:"-"https:"-"https:"-"https:"-"U.S" positions at Forschungszentrum Jülich in Germany
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this position is located: https://www.fz-juelich.de/en/pgi/pgi-7/research-groups-1/ag-gunkel-1 Your Profile: Completed Master degree in Physics, Materials Sciences or Chemistry, with a good track record
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Your Job: Create confined electronic systems, based on state-of-the-art thin film technologies and subsequent delamination of atomically defined oxides. Master the transfer process for
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Master Thesis in Physics or Materials Science: Remanent phase shifter based on memristive technology
website: https://www.fz-juelich.de/en/pgi/pgi-7 and the website for the mentioned project (in German): https://neurosys.info/projekte/ Your Profile: Completed qualification for enrollment in the project
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is located: https://www.fz-juelich.de/en/pgi/pgi-7/research-groups-1/ag-gunkel-1 Your Profile: Completed qualification to enroll for the Master thesis in Physics, Materials Sciences or Chemistry, with
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located: https://www.fz-juelich.de/en/pgi/pgi-7/research-groups-1/ag-gunkel-1 Your Profile: Completed qualification to enroll for the Master thesis in Physics, Chemistry, or Materials Sciences, with a good
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Master`s program in the field of process engineering, chemical engineering, energy engineering, environmental engineering, or a related field Good knowledge of thermodynamics, process engineering, and
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Germany Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research
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program the memristors Operando characterizing the device electrically and morphologically using conductive atomic force microscopy (C-AFM) Access and active control of advanced experimental setups Your
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two companies. The project has partners from eight different EU countries. All 15 PhD projects are within the overall theme of neuromorphic computing and analog signal processing, targeting applications
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular