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in AI and cybersecurity to develop novel solutions to cyber-resilient AI for the benefit of Swedish industry and society. The vision is to make Sweden a role model in secure trustworthy AI by
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involved in several of these cutting-edge research projects and in the societal transformation that they entail. We offer a broad range of courses and study programmes to match the skills in demand. We hope
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scaling model sizes, training budgets, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on
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of electrodes and catalysts for low temperature fuel cells, electrochemical hydrogen generation, batteries, as well as heavy metal removal from aqueous solutions. By designing well controlled model systems in
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instruments used for field measurements. data entry, error checking, data management and GIS work/analysis. organizing and implementation of fieldwork for students. Qualifications We are looking for you with
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generation, batteries, as well as heavy metal removal from aqueous solutions. By designing well controlled model systems in combination with powerful analysis and characterization techniques, improved
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model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed direction of the PhD studies will be discussed and decided jointly with
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-on workshops, bootcamps, hackathons and technical courses using HPC and AI infrastructure. Align training activities with MIMER’s compute services and expert support model. Ensure that training content is
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science, nanotechnology, life science and energy research with world-leading industrial research and development projects. The division of Condensed Matter and Materials Theory constructs models for and
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and CH4) from headwaters, and use of machine learning and process-based model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed