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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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, Computational Biophysics, or a closely related field Strong programming skills (e.g., Python, C/C++) Knowledge of machine learning frameworks (e.g., PyTorch, TensorFlow) Very good English language skills, ability
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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skills, ability to communicate ideas and results effectively Independent and solution-oriented work ethic Interdisciplinary mindset and enthusiasm for teamwork Ability to work in a multi-cultural, multi
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science. The PhD students are supervised by a supervisory panel consisting of a panel chair, a professor from the natural sciences and a professor from information/computer/mathematical science. The DASHH graduate
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below 30 mK and allow the application of magnetic fields to the sample. We characterize the magnetic properties of the quantum systems using standard spectroscopic detection schemes and electron spin
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storage is an advantage. Ability to work independently with intrinsic motivation and initiative in a team. Joy and expertise in complex challenges in reaction engineering and thermodynamics. Competence in
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results-oriented work, the ability to work in a team, social skills and the ability to collaborate across disciplines on an equal footing. We offer you a demanding and challenging work environment and a
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Hamburg, European Synchrotron Radiation Facility ESRF in Grenoble, and European X-ray Free Electron Laser EuXFEL in Schenefeld Present your scientific results at conferences and in publications Requirements
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spoken English skills self-motivated, independent and responsible working style and high organizational skills ability to work in a team and willingness to integrate into an international working