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to have experience with: Phase equilibrium calculation algorithms and their integration into CO2 capture simulation Thermodynamic modeling of phase equilibrium and thermophysical properties related to CO2
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techniques for integrating such solutions into modern SDV middleware. Responsibilities: Conduct research in runtime analysis and reconfiguration of in-vehicle TSN networks. Develop algorithms and prototypes
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solutions and policy impacts. You will design and implement machine-learning algorithms that interact with your simulation framework for scenario discovery, building surrogate models of simulation outputs
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will develop in the position; it is expected that you have previous experience on each of them: Develop and implement CFD models to simulate the behavior of PRO systems. Apply ML algorithms to optimise
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learning representations and improve their interactivity. Make AI explanations more understandable Machine learning algorithms often appear as complex black boxes and much research goes into visualizing
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key responsibilities will include: Designing and implementing advanced LabVIEW and C++ based control software for our HS-DAFM platform Developing specialized signal processing algorithms and circuits
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statistical and machine learning techniques for dynamic energy system modelling Develop advanced optimization algorithms for building energy management and control (e.g., MPC, RL) Develop and evaluate digital
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transformative advancements in scientific discovery. Responsibilities As a Senior postdoc, you will be able to contribute to designing your own project focusing on implementing cutting-edge algorithms for graph
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integrate machine learning algorithms and Earth System Models to emulate carbon processes in the ocean connected to the biological activities. You will be enrolled in DTU’s Section for Oceans and Arctic and