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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning
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heritage, materials science, and physics. Supported by 19 countries, the ESRF is an equal opportunity employer and encourages diversity. Context & Job description Thesis subject: Sub Nanosecond Imaging
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to molecular indicators alone. We have now at our disposal a novel nanometer resolution 3D correlative imaging pipeline that combines light and X-rays and allows us to utilise the cellular landscape as a useful
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for imaging blood flow dynamics in cancer using nuclear imaging techniques. This position involves pursuing a PhD thesis under the supervision of Dr. Pellico as part of the project "NanoFLOW” funded by
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Overview: 1x full time, fixed term (17 months) position available within the School of Science Salary Academic Level A + 17% Superannuation Based at the City campus but may be required to work and/or be based at other campuses of the University. About You The Research assistant will undertake...
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–2 years, total 3–4 years) on deep learning for medical imaging. This DFG-funded project focuses on developing deep learning methods for medical and scientific imaging. The Professorship for Machine
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are seeking a Ph.D. student to join our multidisciplinary team developing a radical solution for better detection and treatment that uses ultra-thin snake-like robots and advanced optical imaging techniques
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background in Earth Sciences or another appropriate field. You will work on the project “Nanoscale Rock Magnetism and Magnetic Imaging”. Your job This study is part of the ERC Consolidator project SPARK, which
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Dortmund, we invite applications for the Research Group Analysis of Microscopic BIOMedical Images (AMBIOM): PhD Candidate (m/f/d) The position is part of the DFG-funded project: ComplexEye - a 96/384-well