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read the full job description and apply at: https://workingat.vu.nl/vacancies/phd-in-reconfigurable-computing-for-heterogeneous-ict-infrastructures-amsterdam-1197676
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team of experienced researchers in imaging, machine learning, oncology, and pathology. We do not discriminate on the basis of sex, gender, belief, culture, place of birth or occupational impairment when
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period of 4 years at the Eindhoven University of Technology, Department of Mathematics and Computer Science. The student’s working locations will be at Eindhoven University where the PhD candidate will be
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identification and machine learning. The key challenge is striking a balance between, on the one hand, modelling the physical, dynamic and nonlinear behavior of the components with sufficient physical accuracy
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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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Computational Fluid Dynamics (CFD) models; data-based models determined from training/calibration data by system/parameter identification and machine learning. The key challenge is striking a balance between, on
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learning with a background in robust and embedded control, aerospace systems and physical modelling, as well as in artificial intelligence-related concepts, computer vision and numerical optimisation
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Challenge: How do we enable continuous robot learning in shared
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ALS patients). The main objective of this PhD project is to develop gene-targeted therapies for newly identified ALS-associated genes. The doctoral candidate will acquire a broad set of skills
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applications from individuals with experience in: Deep learning Medical image computing (preferably x-ray imaging) Computationally efficient deep learning Deep learning model generalisation techniques