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increasing demands for climate neutrality and sustainability. It is imperative to build, demolish, and rebuild with a clear purpose and in a smarter way. A substantial share pf the global energy usage is
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materials. This class of materials has unique properties which make them promising candidates for next-generation electronic devices, energy storage systems, sensors, and catalysts. However, they also pose
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energy, energy storage, electronics, medicine, sustainable manufacturing, etc. The main focus for the advertised position is novel machine learning method development. However, you will be part of a larger
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. The research group led by Martin Enge is specialized in methodology-driven analysis of patient data, especially in the field of single-cell multiomics. We are a multidisciplinary group with expertise in both dry
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for one to two PhD students in analytical chemistry to develop analytical methods for single cell analysis and mass spectrometry imaging using direct infusion mass spectrometry. The PhD candidate will work
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. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material
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objects, e.g. when doing a building inventory, and providing mappings and sharing data between stakeholders. These interfaces will be based on needs and requirements identified in the project use cases
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to make a difference. Do you want to be involved and contribute to our development? Together, we can create a sustainable future through knowledge and innovation. We believe that knowledge and new
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planning. Read more here: https://www.chalmers.se/en/departments/m2/research/transport-energy-and-environment/assessment-of-shipping-pressures-and-impacts-on-the-marine-environment/ The position is based
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/about-slu/work-at-slu/ Name of research project: Attitudes, needs and preferences in the energy transition of Sweden’s food system Description: Rural Development Qualifications: The PhD candidate will