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with researchers at DTU and KTH, you will help develop an integrated decision-support system that: Uses real-time sensor data and AI models to assess risk scenarios. Dynamically recommends optimal
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within the broad topics of modelling tool-workpiece interaction in mechanical material removal processes, zero-defect manufacturing, machining system performance characterization as well as on-machine and
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competences within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, and
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modelling using existing models and using AI based tools. The focus of the work will be to cater to the needs to high voltage/power in power electronic systems, while avoiding humidity and gas exposure
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sequencing data and optimise editing conditions Execute pooled functional screens to identify synergistic gene combinations Validate hits with targeted assays and in‑vitro models Contribute to B.Sc./M.Sc
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the absolute forefront of observing and modeling two of Greenland’s largest glaciers -- Jakobshavn Isbræ and the Northeast Greenland Ice stream (NEGIS). You will use GNSS data on ice surface and bedrock
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-cutting and bending to break the glass panels. The project will involve the establishment of a numerical model and the acquisition and analysis of data from physical measurements in the production
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with methodologies such as AI-assisted evidence synthesis and quantitative health impact assessment and become part of an interdisciplinary research environment with strong links to DTU Compute and the
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to mechanical forces. We work with leading international groups on modeling and also conduct simulations at DTU. Our overarching goal is to understand and predict the mechanical behavior of metals during plastic
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functional priors from billions of years of evolution; how to compress measurements with controlled mixtures of molecules; and how to align models of laboratory experiments with observational human biology