13 numerica-optimization Postdoctoral positions at Chalmers University of Technology in Sweden
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apply machine learning and optimization algorithms in order to achieve the design of such nanophotonic structures. As a postdoc you will be part of the Condensed Matter and Materials Theory division, a
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graphene-based field effect transistor sensors with biological receptors for infection biomarkers, and optimize this technology for diagnosing infections in the wound settings. As a postdoctoral researcher
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methods and models for digital twin simulation in autonomous shipping, and integrate them into a cohesive model. Energy optimization: Develop a dynamic energy optimization model for hybrid and electrified
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Do you dream of organic and holistic ways of automating the design, motion optimization and control of legged robots? With this postdoc position, you have the opportunity to be a part of the ongoing
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inverse design, optimization, and/or machine learning for designing and optimizing devices in integrated photonics, which will be subsequently fabricated and tested by our experimental partners in Metapix
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to maintain system stability during major disturbances (e.g., generator outages, transmission line failures, or system separations) Developing methodologies for optimally allocating these services based
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are looking for a curious and driven postdoctoral researcher to join a project focused on improving how we study and optimize medical treatments. The work centers on advancing a vessel-on-a-chip platform—a
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characterization of algal food protein ingredients, ensuring high yield and quality. The latter will be optimized from several aspects, including e.g., in vitro nutrient digestibility and volatile compound profile
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yield and quality. The latter will be optimized from several aspects, including e.g., in vitro nutrient digestibility and volatile compound profile. The work will be carried out in close collaboration
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite