36 algorithm-development-"the"-"The-Netherlands-Cancer-Institute" Postdoctoral positions in United Kingdom
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Project The Adaptive Design for AI-Driven Processes in Transforming Dynamic Landscapes (ADAPT) project develops scalable, data-driven design methodologies that transform dynamic environmental processes
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modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy systems. Responsibilities will include programming, analysing and interpreting data, and
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About Us We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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image acquisition capabilities. Together, we are developing next-generation Ai-assisted robots for colorectal cancer treatment. About The Role EndoTheranostics aims to transform the screening and
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application. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy
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of Oxford. The post is funded by the National Institute for Health and Care Research (NIHR) and is fixed term for 24 months. The researcher will develop multi-sensor 3D reconstruction algorithms to fuse
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in our team. Learn more about us here: https://sc.cs.univie.ac.at/ Your future tasks: Active participation in research, teaching and administration, which means: You develop your independent research
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We invite applications for a full-time Postdoctoral Research Associate to join the new Data-Driven Algorithms for Data Acquisition (DataAcq) project. This is a timely project developing new
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transport for inverse problems One of the central topics of the research projects is the further development of theory and methods for the concept of optimal transport for inverse problems. Optimal transport
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Martin and Professor Daniel Paulusma from Durham University and Professor Vadim Lozin from the University of Warwick. The general aim of the project is to develop algorithmic meta-classifications, which