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Field
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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning
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Skills/Qualifications Must have, at the start of their PhD programme, a Master (or equivalent) degree in Mechanical Engineering, Physics or Photonics with solid knowledge of optics and its applications
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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of data scientists, software engineers, and experimental researchers on topics including: Developing multi-scale and multi-modal representation learning methods for scientific imaging data (e.g., SEM
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science and information science techniques. Several areas of computer science and mathematics play important roles: data management and engineering, machine learning and data analytics, signal and image
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computer science, bioinformatics, physics, engineering, computer science, statistics, mathematics) or in health science related fields (e.g. medicine, veterinary medicine, pharmacy, life sciences) is mandatory
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with reducing and oxidising gas-phase species (e.g. laser-based imaging diagnostics, setup of model reactors, modelling of underlying reactions, multi-scale simulation of reactive fluids, computational
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molecular cell identification and single-/two-photon imaging techniques. You will work at the Leibniz Institute for Neurobiology (LIN) with Prof. Stefan Remy and in close cooperation with Dr. Janelle Pakan
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into an interdisciplinary research environment given by the university institutes and the cooperation partners in industry and science. The advanced Manufacturing Engineering paradigm for next generation factories is
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biomedical engineering, bioinformatics, biotechnology, molecular imaging and biomaterials. Training within the RegSci PhD programme takes place across three levels: students carry out their own research