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. The system will include: A very compact, ultra-low-power analog front-end (AFE) to sense neural signals. An on-chip neuromorphic processor to convert the neural data into spike-based encoded data and
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specifically examine gas corrosion mechanisms in connection with copper and silver, while investigation also will be carried out on specific electronic components such as chip resistors, IGBT substrates etc
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in connection with copper and silver, while investigation also will be carried out on specific electronic components such as chip resistors, IGBT substrates etc. Work will also involve electrochemical
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to go to the field (poultry farms and slaughterhouses) to collect samples. You are highly motivated, curious, and have strong analytical and problem-solving skills. You are not afraid to ask questions and
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Neural Networks (SSM-SNNs). The project includes the co-design and integration of a RISC-V processor for hybrid neuromorphic computing. The research aims to develop ultra-low-power computing chips
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RISC-V ISA. Your project will involve developing and verifying time-predictable on-chip communication to support multicore systems and provide a basis for real-time automotive functionalities
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(e.g., single-cell and bulk RNA-seq, ATAC-seq and ChIP-seq) and computational data analysis is considered highly advantageous. Selected references: Jakobsen et al., 2024. MYC activity at enhancers drives
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, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction
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time and emission in the low-loss telecom C-band. We are looking for up to three PhD students, who have excelled in their studies, to join our team working towards demonstrating chip-scale quantum light