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: System-Level Impact Analysis of IAM using Agent-Based Models Supervisor: Prof. Dr. Regine Gerike, Chair of Mobility System Planning and co-supervised by at least one additional professor
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, tumor biology, and molecular biology. Our research aims to develop creative and innovative strategies to characterize new classes of molecular imaging agents and advance those with translational potential
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researcher with a strong interest in investigating multi-omic and multimodal brain imaging signatures of glymphatic function in the context of aging and cerebral small vessel disease (sCVD). About the Research
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at obtaining further academic qualification (usually PhD). Research area: Systems of interacting particles are ubiquitous in natural and social sciences. Typically, they comprise many agents that, through intra
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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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. The successful candidate should have experience with advanced astrophysical data analysis in the context of multi-mission and multi-wavelength observations including e.g., Fermi-LAT and imaging X-ray
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interest in agentic AI, online learning and optimization, and applications in economics. The full-time positions (100%) are initially offered for two years, with the possibility of extension, depending
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the RTG. This PhD project aims to develop a multi-physical simulation framework for various localization sensors (such as e.g., GPS, IMU, SLAM, RTK) that determines the vehicle’s position, explicitly
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to potential reusers via suitable logistics chains. This includes the integration of individual stakeholder requirements and their representation by digital agents for the intelligent linking of logistics orders
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-motivation and interest to learn new skills Great to have: Experience programming in Python, Julia, or C/C++ Experience with Mathematica Experience with finite element methods, agent-based simulations, and/or