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potential for exploiting temperature gradients for producing electricity and predict their long-term performance under real operating conditions. The project also includes modeling of heat transfer and
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, protocols, and data standards across collaborating institutions and scales. This collaboration will support the generation of coherent, high-quality datasets and enable the development of predictive models
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-driven analysis and modeling. These are interdisciplinary positions that combine environmental engineering, materials science, and sustainability assessment. We are looking for motivated and forward
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-scale research facilities, such as synchrotrons, for EXAFS and high-pressure XPS measurements. We also work in close partnership with the CatTheory group, using DFT-calculations and microkinetic modelling
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collaboration with survey providers in Denmark and Germany. Analyzing survey data using quantitative methods such as conjoint analysis, regression modeling, and causal inference techniques. Writing academic
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the opportunity to co-supervise BSc, MSc, and PhDs, and we expect you to actively engage with the team and make your expertise available to your colleagues. We seek a talented physicist with an
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empathic person with a PhD degree in mechatronics engineering, MEMS, neurotechnologies, applied physics, nanotechnology, neural interfaces, biomedical engineering or similar. Advanced skills in electronics
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from water samples compared to tissue samples for early detection of disease. Another is to perform “what if” scenario modelling to compare suitability for different sampling protocols in farms in non
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the department’s competencies in advanced modelling and control of buildings' energy systems, e.g., heating, cooling, and ventilation systems using IoT technologies. You will collaborate closely with academic and
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describe concrete examples of your own relevant previous work involving these topics). Experience with modelling is an asset. As a formal qualification, you must hold a PhD degree (or equivalent). We offer