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Field
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, specifically methods that combine machine learning and optimization with physics-based simulation and/or physical constraints and translate these methods into impactful industrial applications. The position is
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approach (CPlantBox) to mechanistically simulate plant growth, plant-soil interactions and the rhizosphere microbiome. You will contribute to model development and apply it to disentangle the role of root
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position in the area of machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement
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of teleoperation, work organization, and simulator development for automated vehicle fleets in public transportation. About Us The Chair of Ergonomics, headed by Prof. Dr. Klaus Bengler, conducts research and
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learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work with, enhance
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-scale atomistic spin simulations to evaluate energetics, thermal stability, non-linear dynamics and stochastic response, and to propose materials engineering routes (composition, doping or strain
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of the linear-scaling Wannier optics approach, with the goal of enabling predictive simulations of optical excitations in large and structurally complex systems. Particular emphasis lies on the theoretical
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Your Job: This PhD project focuses on modelling and simulating future gas grids, exploring transformation pathways, and developing cross-sectoral simulation frameworks to support informed decision
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related field Strong proficiency in English Capability to work independently and collaboratively Preferred Qualifications: Experience with thermal transport simulation, reactive-transport simulation
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independently and collaboratively Preferred Qualifications: Experience with multi-phase flow simulation, reactive-transport simulation, groundwater flow simulation, and model development Basic knowledge