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Field
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principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put
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science, engineering, physics, mathematics or a similar domain. There is a strong preference for an applicant with a biomedical background. Experience with medical image processing, histopathology, computer vision
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learning, or signal processing; familiarity with microscopy data is an asset but not required Interest in foundational machine learning research with applied impact in scientific imaging Demonstrated
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through a self-learning chip prototype, improving performance and durability in automotive applications. Specifically, this PhD project focuses on memristive materials as electronic realizations
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that exhibit emergent turbulent behaviors, and (2) disordered optical media that process information through complex light scattering patterns. Using advanced imaging, machine learning techniques, and real-time
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engineering, electrical engineering, data science, or a related field. Skills in embedded systems development, electronics, or IoT (C/C++, Python, Arduino/ESP32, etc.) OR in machine learning and sensor data
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within a Research Infrastructure? No Offer Description Topics In the Computer Systems Lab, we aim to hire multiple PhD students on national and international research projects in the domain of man-at
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8 Sep 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Computer science » Computer hardware Computer science » Digital systems Engineering
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about biomedical research or cell biology? Ready to make groundbreaking contributions to understanding ciliopathies through innovative machine learning approaches? Join the Cilia-AI consortium, and embark
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master’s degree in electrical engineering, computer Engineering, or a related field. Strong background in mixed-signal circuit design and simulation. Familiarity with IC design tools and tapeout flow and