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, have been optimized for fossil fuels for more than 140 years but the new fuels have properties that can enable a more efficient operation. Advanced combustion processes like HCCI, SACI and PPC will be
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on turning high-quality (smart meter, grid, or weather) data into actionable insights and better-informed decisions through advanced data analytics. When this energy data is shared, it can be exploited by
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students. In the project you will: Perform longitudinal proteomic and degradomic analyses of wound healing models and clinical samples. Identify proteolytic cleavage sites and post-translational
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interactions. Clone and express top candidates using high-throughput cell-free and bacterial systems. Perform structural and biophysical characterisation of designed enzymes Perform functional characterisation
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research facilities include modern laboratories and several core facilities (NGS, single cell and spatial aspects, high-throughput robotics tec.) shared between the 26 research groups at BRIC and the
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that merge thermo-fluid dynamic laws, deep learning, and experimental data. A central goal is to overcome current limitations in TES operation and optimization, enabling discovery of new high-performance and
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PPC at high load where knock will severely limit SI operation. However, at low load CI combustion cannot be used but SI works well. By combining SI and CI, as well as the more advanced concepts of HCCI
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Computer Vision There is growing trend towards explainable AI (XAI) today. Opaque-box models with deep learning (DL) offer high accuracy but are not explainable due to which there can be problems in
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candidates using high-throughput bacterial systems and cell-free protein synthesis. Perform structural and biophysical characterization of designed proteins (e.g., CD, SPR, BLI). Perform validation
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generally require near-unity efficiency and a high single-photon purity as well as indistinguishable photon emission. You will participate in a project team with several senior researchers and Ph.D. students