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optimization. We are looking for a candidate who is motivated by both technical curiosity and making a real-world impact. Ideally, you: Have experience with AI models (e.g., graph neural networks, supervised
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to determine the molecular structure and function of a TZ sub-complex consisting of 3-5 proteins. You will monitor the gating mechanism of TZ in cellular models such as RPE1 or cultured dopaminergic neurons by
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intelligence. This PhD project will leverage the power of field-programmable gate arrays (FPGA) to deploy machine learning models on the edge with low latency and high energy efficiency. This added intelligence
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wearable and ambient IoT sensing systems for activity and health monitoring. Implementing embedded AI models for anomaly detection and behaviour analysis. Working on digital twin and serverless IoT
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of the following areas and an interest to develop within others: Protein chemistry Enzyme kinetics and kinetic modelling Experimental physical chemistry Electrochemistry Assay development and
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designs, building effective and conceptual models to inform our theoretical understanding, and developing code and theory frameworks to address new topological phenomena. Depending on the project’s results
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restraint conditions. A key goal is to develop both a sensor system and a prediction model for the short- and long-term deformation behaviour of concrete. These tools will be applied to full-scale structural
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correlate with operational and environmental factors. This research will be conducted in close collaboration with academic and industrial partners. In particular, the project includes secondments at: Vestas
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collaboration within e.g. nutrition, chemistry, toxicology, microbiology, epidemiology, modelling, and technology. This is achieved through a strong academic environment of international top class with
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apply a range of single cell genomics technologies on clinical biopsies combined with bioinformatics, and a range of targeting strategies in mouse disease models. As a member of the Center of Excellence