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statistical methods for modelling and data treatment engage in teaching, innovation and advisory activities in relation to food technology, food chemistry, and food nutrition in a broad sense. Teaching
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could include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success Policy Implications and Recommendations
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privacy guarantees. This PhD project will develop scalable, privacy-preserving coordination models that jointly optimize DER integration, electrified loads, and data-center flexibility — ensuring fairness
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model checking. You should be well versed in basic statistics and practical programming skills is a must. Knowledge about the inner workings of GenAI would be nice but not necessary. You must have a two
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generalization. However, existing machine learning theory does not fully explain this behavior, leading to the development of new approaches. A promising explanation is that models are implicitly regularized
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qualifications around data-driven digital twins, energy systems modelling, forecasting and control. Some prior knowledge on distribution grids and methods for grid services is preferred. Moreover, the following
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of efficient biocatalysts that could be applicable for our different bioreactor platforms. Project description. For technical reasons, you must upload a project description. Please simply copy the project
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recognised business school with deep roots in the Nordic socio-economic model. Our faculty has a broad focus on societal challenges, and we have earned a reputation for high-quality disciplinary and
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, laboratory testing and data augmentation. Your main goal will be to develop predictive models for assessing the degradability of bioplastics, facilitating a transition to more sustainable materials. More
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TZ complexes, you are expected to determine the molecular structure and function of a TZ sub-complex consisting of 3-5 proteins. You will monitor the gating mechanism of TZ in cellular models such as