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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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and development of associated sensing and estimation algorithms Path planning based on medical imaging data (e.g., MRI, CT, angiography) Development of AI-based control methods for continuum robots
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will: Develop and implement model-based and data-driven (AI) optimization algorithms for battery charging Integrate physics-informed models and data-driven tools to design health-aware charging protocols
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biodiversity assessments and methodological development at Aarhus University. Your profile Applicants should hold a PhD in ecology, population or conservation genetics, evolutionary biology, bioinformatics, or a
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: Implement and optimise deep learning–based models for the quality control and real-time assessment of concrete constituents within in-line production. Develop and train predictive algorithms based
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is an advantage but is not required. A strong interest in evolutionary principles, ecological principles, and data science are expected. The applicant must be interested in working in
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is to develop machine-learning-based algorithms for transmitter pre-distortion and receiver post-distortion architectures that enable distortion-free quantum communication systems. A key focus will be
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on stability. Testing the model in standard stirred tank apparatus Refining the model to allow predictability between different types of apparatus. Defining an algorithm for testing enzyme stability
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simulation tasks. A key objective will be the development and testing of algorithms for gas hydrate phase equilibrium calculations. CapSim seeks to advance CO₂ capture simulation technology by enhancing
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describing the effect of conditions on stability. Testing the model in standard stirred tank apparatus Refining the model to allow predictability between different types of apparatus. Defining an algorithm