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Field
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how variations in mould structure, porosity, and surface characteristics affect radiative heat transfer and casting performance. Phase-field modelling will also be used to simulate defect formation and
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the right one for you! This is a fully funded PhD position to develop micromechanical models of high-pressure die-cast aluminium, a unique opportunity for a motivated individual to work in a collaborative
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, evaluating, and fine-tuning machine learning models (e.g. deep neural networks) to segment underwater scenes and classify anomalies. The work will explore the use of virtual environments and synthetic datasets
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simulation framework to model the coupled USV-UUV system, enabling safe experimentation before field deployment. Field validation: Conducting field experiments (e.g. in a harbor and offshore test site) where
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consist of the following: Mathematical analysis of ecological and eco-evolutionary models, involving pencil-and-paper calculations; Computer simulations of more complex models which do not easily lend
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-cycle fatigue. The research methods are based on both small-scale and full-scale experimental testing and on Finite Element Modelling. Are you motivated to take a step towards a doctorate and open
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work with fish behavior, engineering geology, turbine design, variable speed operation, etc. It involves fundamental research including a literature study and CFD modelling with the use of real case
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. The project requires knowledge of real options analysis, quantitative modelling skills and understanding of the CCS value chain. The aim of the project is to: Develop methodologies to assess investment
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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on software and other IT products. We also offer access to additional employee benefits through our Terms of Employment Options Model. In this way, we encourage our employees to continue to invest in