47 computational-physics-simulation-"Prof"-"Prof" PhD positions at Forschungszentrum Jülich in Germany
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] method within the MiMiC multiscale simulation framework Develop and integrate a communication interface between quantum computers (via Qiskit) and classical HPC resources Validate the QCS-MiMiC
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) with excellent grades in computer science, materials science, physics, or a related discipline Practical experience in data science, including the application of machine learning (ML) methods or large
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: A completed university degree (Master’s or equivalent) with excellent grades in computer science, materials science, physics, or a related discipline Practical experience in data science, including
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on investigating the change in the catalysts surface under relevant process conditions using spectroscopic analysis methods. Your task will include: Application of established and novel methods for the preparation
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Your Job: Explore bio-inspired algorithms through simulation—both numerical and circuit-based—and experiment with existing hardware, including CMOS and memristor circuits. Additionally, will need
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student projects and BSc/MSc theses Your Profile: Master’s degree in physics, electrical/electronic engineering, computer science, mathematics, or a related field Strong background in machine learning
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in physics, electrical/electronic engineering, computer science, mathematics, or a related field Strong background in machine learning, particularly deep learning and optimization methods Excellent
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the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy Materials and Devices – Structure and Function of Materials (IMD-1) to establish a data-driven
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-free double perovskites Your Profile: Master`s degree in theoretical or computational physics, chemistry, materials science or similar fields Familiarity with atomistic simulations, high-performance
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
DNA thermodynamic database, coarse-grained simulations of DNA motifs, and existing experimental data to establish an AI model that is able to guide the construction of desired secondary structures