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assessment criteria, which will be benefit are: Knwoledge and experience of cloud, monitoring & automation foundation Experience in foundation models and Large Language Models (LLMs). Experience in teaching
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. Experience with high-performance computing, cloud computing, GPU acceleration, or distributed data processing. Experience participating in international scientific collaborations and/or multi-institutional
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. Experience with high-performance computing clusters or cloud-based Earth observation platforms (e.g., GEE Python API). Experience with airborne lidar data processing and canopy height modelling. We will place
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Resilience and Confidentiality in the Cloud), a EUR 2.5M collaborative project between KTH, Saab, Nvidia, Ericsson, Red Hat, CanaryBit and RISE, building next-generation secure and dependable AI for critical
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, edge/cloud architectures or industrial digital infrastructure model-based systems engineering or system-of-systems design interdisciplinary collaboration with industrial partners scientific publication
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researchers focussing on modelling, estimation and prediction related to battery systems, ranging from details on micro-scale in cells to cloud calculations for fleets of electric vehicles. About the research