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Are you passionate about advancing sustainable mobility solutions? Do you enjoy working at the intersection of artificial intelligence, optimization, and energy management? We invite applications
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/thesis: Industry-/collaboration PhD student in optimized off-road driving in forests Research subject: Soil science Description: We are looking for an industry/collaboration-based PhD student to develop a
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exists for researchers to design and improve animal tests. These limitations hinder the development of optimal experiments and incur cruel animal suffering and killing.The position is two years and you
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of research that has been developed at the Unit is the proposal and computational implementation of novel formulations for the conceptual design of mechanical components and structures via topology optimization
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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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of numerical modelling (e.g. CFD, FEA, FSI, optimization, ML), but we are also involved in experiments and real-life monitoring to support our findings. Besides research, our division is actively involved in
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is meritorious for future research duties within academia as well as industry/the public sector. The postdoctoral project will be focused on the design of optimized generation schemes for tailor-made
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environments ensure safe operations Our activities are primarily related to the development and application of numerical modelling (e.g. CFD, FEA, FSI, optimization, ML), but we are also involved in experiments
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from 3D-optimized end-walls provided by additive manufacturing (AM). The project outcome will solve the urgent need for CO2 reduction from air traffic. Research environment You will join the Fluid