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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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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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. The goal is to create a design guide for municipalities and consultants, based on findings from the Chalmers Gårda raingarden pilot , to effectively treat polluted stormwater. The study will identify optimal
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deposition, its characterization and optimization. Design sensor layout and evaluate materials involved, from the standpoint of bio compatibility. Functionalize graphene devices, in collaboration with chemists
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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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analyses, as well as optimizing growth on waste biomass and conducting large-scale fermentations. You will be part of an interdisciplinary team at Chalmers and collaborate regularly with industry experts
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that play a key role in biomineralization. Your work will include microbiology experiments, genetic engineering, protein purification, and biochemical analyses, as well as optimizing growth on waste biomass
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capabilities. You will explore the electrochemical response of operando cells compared to typical lab-based cells and optimize electrode geometires.This project is part of Batteries Sweden (BASE) and with
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We are seeking a highly motivated doctoral student to develop ship physics-integrated machine learning models for real-time prediction and optimization of wind-assisted ship propulsion systems