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Field
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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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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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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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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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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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algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation
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of visualization and multimodal machine learning. Admission requirements The general admission requirements for doctoral studies are a second- cycle level degree, or completed course requirements of at least 240
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and image analysis (MATLAB or Python), machine learning techniques, and basic programming/coding will be a plus. Fluency in English is mandatory. Willingness to work in an inter-cultural and
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, machine/deep learning (Pandas, SHAP , TensorFlow, etc.) and specific to image analysis, statistics, simulation, cloud environments (Kubernetes type, Docker-compose, virtualization, etc.), 3D environments