40 algorithm-development-"Multiple"-"Prof"-"UNIS"-"DIFFER" positions at Aalborg University
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Foundation, Lundbeck Fonden, and the Danish National Research Foundation, who aim to advance materials development for Power-to-X. The successful candidate will be part of the CAPeX Academy together with 100
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research and technology stakeholders. The vision of SonicEye is to develop multimodal navigation by enabling AI-powered echolocation and safe decision-making for autonomous robots in uncertain environments
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treatments based on the prediction of expected outcome. Initially, we will develop in vivo measurement techniques and perform in vitro and ex vivo experiments to obtain model inputs. Hereafter, mathematical
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such as wind turbine applications, heavy duty truck chargers, medium voltage hardware technology for megawatt marine applications. The hardware tasks include development of new solutions for megawatt
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investigating how creative, experimental and practice-based learning cultures can be developed in vocational education. In relation to research, applicants should document: Experience, skills and achievements
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01.01.2026 or soonhereafter in Aalborg. The position is available for a period of 12 months with the possibility of extension. PLAN conducts research and teaching on development and planning in a broad sense
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this project will feed into a central model (developed in a parallel CEBE work package) linking the parameters of constitutive material model to physical and chemical properties across scales supported by AI
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explore large genomic datasets. Your tasks will include quantitative data analysis, sequence processing and evaluation, and pipeline development and automation. It is expected that you will lead the writing
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the development of electro-thermal models, state estimation methods, and control strategies to support efficient battery operation and thermal management. The Postdoc will collaborate with colleagues at AAU Energy
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of Computer Science (CS) and Sustainability and Planning (PLAN). This position focuses on the machine learning methodology of the project, aiming to: Develop probabilistic spatio-temporal models that integrate