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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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an efficient AI foundational exploration of the molecular space. How can we bias the generative models towards desirable molecular properties How can we integrate generative AI models and different molecular
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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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Job Description The Institute of Mechanical and Electrical Engineering at SDU invites applications for a 3-year PhD position focusing on CMOS implementation of State-Space Model-driven Spiking
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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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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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allocation, start-up/shutdown sequences, and operational planning of PtX systems in response to dynamic market and process conditions. System Integration and Digital Twin Development: Combine experimental
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R Experience with single-cell RNA-sequencing, in particular analysis of data would be an advantage Experience with mouse models and possession of a FELASA B certificate would be an advantage as both
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will be involved in exploring the models, designing, and measureing CMOS-based interface (coherently control, readout, and biasing) to quantum bits: Conduct a thorough review of recent research in