325 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" positions in Denmark
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towards a carbon neutral society. A central component of the position will be cutting-edge research in conception, development and implementation of advanced thermodynamic methods that leverage data-driven
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PostDoc in aerobiological modelling Are you interested in atmospheric science and can you contribute to the development of a new generation of numerical models describing concentrations and
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Power-to-X systems. You will drive the development of system-level digital-twin and optimization methodologies, integrating electrolyzer models with renewable generation, power conversion, energy and
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learning from demonstrations and generalisation to unfamiliar tasks, objects or environments. World models and planning Develop models that predict how the physical world responds to actions, supporting
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-timescale optimization, and integrated Power-to-X systems. You will drive the development of system-level digital-twin and optimization methodologies, integrating electrolyzer models with renewable generation
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largely undeveloped. Addressing this challenge will be the central objective of your PhD research. Responsibilities and qualifications You will develop a physics-based modelling framework for bearingless
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. You will be involved in the further development and application of the FlexSem model system, which is a framework developed by the model group to describe the dynamics of coastal marine systems
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, biochemical engineering, metabolic engineering, and artificial intelligence. The position aims to strengthen KT's research in the engineering of biological systems and the development of new biological
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is the development of hybrid models that combine our integrated national model of Denmark with AI-based methods to improve predictions of floods and droughts. We seek to enhance our ability to predict
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language models. This research will address critical challenges that arise when multiple parties jointly train, fine-tune, or deploy large language models in distributed environments while safeguarding both