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Field
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volume correlation processing of images in preparation for image-based modelling Your profile PhD in physics, materials science, computer science, applied mathematics or a related field strong background
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W3 Endowed Professorship for “Hemodynamic Modeling in Atherosclerosis- (f/m/d) KSB Foundation W3 end
and teaching, the professorship will represent functional cardiovascular imaging with a focus on hemodynamic modeling and act as a bridge between basic research, medical technology development, and
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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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science, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time
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the consortium Your Profile: Excellent Master and subsequent PhD in computer science, engineering, biophysics, applied mathematics, computational biology or a related field Proven programming expertise in Python
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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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computer vision methods and their applications Your Profile: Excellent Master’s degree in engineering, computer science or mathematics (or a related field), with a focus on computer vision, image processing
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. Your qualifications: Master’s degree in Aerospace Engineering, Mechanical Engineering, Computer Science, Electrical Engineering, or a related field. Strong interest and commitment to pursuing a Ph.D
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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