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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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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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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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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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be widely implemented in clinical programs. Job description The aim of this position is to develop new chemical methods for the modification of nucleic acids. This includes the design and synthesis
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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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resource recovery pipelines You will contribute to the translation of structural and biophysical insights into technologies or methods for transforming recovered biopolymers into valuable products or process
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understand how materials interact with soft and hard tissue. Willingness to learn and integrate new methods or technologies, such as bioinformatics and 3D imaging processing. Ability to work both independently
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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