354 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Newcastle University" research jobs in Denmark
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description We invite applications for a 2-year postdoctoral position with the possibility of extension. The successful candidate will lead experimental campaigns and data analysis to quantify greenhouse gas
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graph algorithms for optimization under physical constraints Applying graph mining and graph data management techniques Designing computational methods for waste heat reuse and green transition goals
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cross-European research project FOOD-FRAMES. The FOOD-FRAMES project explores how the food information environment across nine European countries affects sustainable food choices, often hindering
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data We are looking for a candidate with a structured working style and the ability to multitask with attention to detail. You must have a positive mindset and willingness to take on both routine and
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workplace here You can read more on Department of Sustainability and Planning here You may obtain further professional information from Associate Professor Troels Krarup, e-mail troelsmk@plan.aau.dk or phone
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, high-throughput screening, and state-of-the-art carbohydrate analysis, to validate computational designs and generate data that advances our knowledge of enzymatic carbohydrate synthesis towards
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The project may address national or international problems, and should do so using appropriate methods, qualitative and/or quantitative. Access to Danish data sources, such as registry data, respondents
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reactors Maintain detailed records of experimental data, process conditions, and system modifications. Publish scientific articles based on data collected during the research, development, and innovation
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requirements using pilot-scale or simulation data Publish high-impact research results and present findings at international conferences Contribute to proposal writing and joint projects with academic and
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charging strategies for lithium-ion batteries. The goal is to integrate model-based (digital twin) and data-driven (AI) methods to design and experimentally validate optimized pulse charging protocols. A