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At the Faculty of Engineering and Science, Department of Materials and Production one or more Postdoc positions in the area of Optimization and Algorithm Design are open for appointment from April
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by: Developing specialized algorithms supported on solid theoretical foundations and with a focus on challenging aspects of very high-dimensional datasets, such as datasets encountered in
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conditions for art and culture. Possible focal areas include AI and algorithmic creativity, digital media aesthetics, data-driven culture, new forms of the dissemination of art, literature, theatre and music
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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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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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investigate new algorithmic principles that make learning agents adapt to non-stationary environments in an autonomous manner. The expected outcomes are new theoretical insights about the algorithmic roots
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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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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
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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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approaches, such as topology optimization, with gradient-free methods, such as genetic algorithm, in order to exploit the specific strengths of the two classes of algorithms, and through this reach an ideal