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should hold a PhD in Electrical or Electronic Engineering (completed within the last 5 years) with strong experience in CMOS IC design. The ideal candidate has: Strong background in analog and/or mixed
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. The ideal candidate will have: Experience in developing novel algorithms. Experience in coding in python and preferably C/C++. Experience in frontend engineering, including but not limited
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for screening purposes and cell-based therapies. We will develop methods for modelling missing not at random (MNAR) observations and quantifying uncertainty using Bayesian methods and deep learning architectures
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cycle, from architecture to implementation, of a CMOS-based digital neuromorphic processor. Contribute to the design of a test setup for prototype validation in collaboration with the PhD student who is
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, materials science, and artificial intelligence. What we expect Applicants should hold a PhD in electronic engineering (the degree should have been completed within the last 5 years at most): Strong background
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-waveguide couplings that surpass the time-bandwidth limit of static cavities [Xue2022]. With these components as building blocks, we envision large-scale recirculating circuit architectures [Heuck2023b
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projects. In addition to research, the candidate(s) will contribute to teaching activities within the MSc CREATE programme and the BSc in Civil and Architectural Engineering, with an emphasis on research
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stakeholders We are looking for candidates with: A PhD in electrical engineering, biomedical engineering, physics, or a related field Strong programming skills in LabVIEW, C++, and Python, with
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integrates CRISPR-based genome engineering, quantitative and live-cell microscopy, biochemistry, and computational analysis to dissect how cells sense and respond to replication-associated threats. Recent work