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Do you have experience with modelling structures subjected to dynamic loading? Are you interested in data-driven methods for modelling applied loading? Are you eager to share your knowledge within
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controversy and issue mapping, visualizes the similarities and differences of what issues are analyzed as risks and what methods are employed in the analyses. The map constitutes the backdrop for deeper
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and
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performance in refurbished metal parts and contribute to assessing their environmental impact, including data input for CO₂e analysis tools. Expected start date and duration of employment This is a 1.5–year
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communication skills Following qualifications will be considered as an advantage: Experience with super-resolution techniques and physics-informed machine learning. Familiarity with explainable AI methods (e.g
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(XAI) methods to improve the understanding of key drivers controlling peatland conditions and ecosystem functioning. The research project will primarily focus on implementing and merging analyses
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and a demonstrable interest and experience in the use of computational methods. The candidate should combine the use of quantitative and statistical approaches with (solid) historical analysis, in line
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record relative to career stage Excellent written and spoken English communication skills Following qualifications will be considered as an advantage: Experience with explainable AI methods Experience with
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such as data scarcity, cultural sensitivity, inclusivity, and the need for robust preference optimization methods that go beyond standard fine-tuning. Key research objectives include: Developing Efficient
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cancer or relapse to identify genetic alterations that may be targetable with novel drugs. A main component of this project will be to characterize the genomic and transcriptomic landscape of our patient