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in cost and benefit sharing, minimizing procurement costs and imbalances, and safeguarding both consumer and corporate data. The ideal candidate will have experience or an interest in the following
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of relevant environmental impact categories and utilise these to design optimised trajectories towards absolute environmentally sustainable and healthy food. Qualifications: Have strong expertise in several
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: Specialization in machine learning and computer vision. Strong skills in developing computer vision and AI models. Experience with thermography or thermal imaging. Experience in data fusion and multimodal learning
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experience with: Signal processing Experimental design and data analysis Furthermore, the successful candidate is expected to: Have excellent knowledge of English (written and spoken). Have high self
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the value of the green transition. The project will involve a multi-stakeholder innovation process, utilizing a framework of multiple-loop learning to encourage farmers to reflect on their relationship with
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qualifications will be considered in the assessment: Strong background and interest in dynamic modelling and control Skills and experience with time series analysis and formulation of stochastic dynamical models
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: albert.schliesser@nbi.ku.dk , phone: +4535325401) If it is deemed appropriate from an overall perspective, you must be prepared to be security screened, in relation to international research and innovation
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three years, starting in September 2025. As a PhD student, you will plan, lead, and execute the project in collaboration with your supervisors, contribute to the overall project development, and
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at the extreme nanoscale. This approach holds immense potential for advancing our fundamental understanding of light–matter interactions at the few-nanometer scale, with significant implications for the design of