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Field
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species like birds and bats. Combining PAM with occupancy modeling allows for large-scale ecological studies, assessing both individual species and community responses to environmental changes. In this 4
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, engineers and PhD candidates. The PhD candidate is expected to develop an advanced engineering noise prediction model for efficient computation of sound propagation in a range-dependent atmosphere where
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on these materials, which will be used to implement high frequency and high efficiency IVRs for HPC applications. Objectives:-Fabrication, characterization and modeling of the composite magnetic materials.-Modeling
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hydrogen storage tanks. This PhD vacancy is focused on the finite element modelling and design of the pressurized composite tank, in different operational conditions (static burst, fatigue and impact). The
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tasks will be to: Develop and implement machine learning models for dynamic simulations of renewable power systems Develop comprehensive guidelines for verifying and testing dynamic equivalents Integrate
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aims at developing tools using large language models (LLMs) for the correction of misinformation about climate change in social media. The successful candidate will develop innovative tools leveraging
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the molten pool. However, these models are computationally intensive and impractical for widespread simulations of large-scale part deposition. This project aims to develop a novel FEA-based approach
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development. The successful candidate will contribute to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model
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in bioacoustic technology, such as passive acoustic monitoring (PAM), now enable efficient study of vocalizing species like birds and bats. Combining PAM with occupancy modeling allows for large-scale
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of thermodynamic modelling, process simulation is an advantage. For applicants to the PhD position, you must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent