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Project (PhD Position) – Quantum-Classical Co-Simulation Framework Development for Neurobiological Systems Your Job: The overarching goal is to implement a code for multiscale quantum mechanics / molecular
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implementation on IBM’s ibm_cleveland quantum computer by reproducing recently published benchmark QM/MM simulations [2] Apply the developed code to simulate proton transport in vesicular glutamate transporters
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Experience in processing remote-sensing information and machine learning is an asset Capacity for interdisciplinary teamwork and excellent communication and presentation skills Ability to communicate in
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the application of machine learning (ML) methods or large language models (LLMs) Proficiency in Python programming and confident use of Unix/Linux environments; ideally experience with version control systems (e.g
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a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation
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-driven energy systems research – scalable, transparent, and interoperable. Your tasks in detail: Development of a structured description format for the unambiguous and machine-readable characterization
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control software and machine learning expert. How you will support us: ▪ You will take on responsibilities in the field of control and operation of high-coherence superconducting quantum circuits, with a
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, the student will collaborate with researchers who apply data assimilation and machine learning methods to the developed models. Your responsibilities: Analysing a global compilation of paleomagnetic sediment
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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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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High