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Field
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one of the following areas: scientific programming and data analysis (e.g., Python, R, C++, MATLAB), computational modeling, imaging and sensor data processing, bioinformatics, systems biology, or
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, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power
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in quantum clocks and sensors. The network offers international secondments, joint training events, and close interaction with an international cohort of doctoral fellows. The doctoral candidate will
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include: Applying controlled stress treatments Phenotyping stress responses with various sensors and gene expression studies in real-time PCR RNA sequencing and transcriptome analysis with corresponding
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materials exhibits prompt changes in magnetic, electronic, or optical properties upon stimuli and are promising candidates for applications in memory devices addressable by light, optoelectronics, and sensors
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Experience in designing software systems General overview and understanding of the working principles of sensors and robotics Applicants are invited to submit a research plan outlining their approach
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Network, aims to develop an energy-efficient compute-in-memory (CIM) architecture using gain-cell memory for real-time edge learning, addressing power, latency, and memory bandwidth issues with reliable
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, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power
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the Internet of Things (IoT), where networked sensors and actuators enable real-time adaptation to environmental changes. Consider a self-adaptive IoT network such as a smart home that autonomously manages
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neuromorphic ultra-low-power active sensor readout and processing at the edge. The chip design will enable online learning capabilities, aiming at modulating the spatio-temporal filtering properties with