Physics-informed and physics-constrained machine learning for next generation imaging

来自格拉斯哥大学和“大图像数据”公司DotPhoton的一组研究人员。这名学生将成为一个多学科团队的一员，该团队由物理学家和致力于新型成像技术的计算机科学家组成，其中包括致力于对成像问题的物理进行编码的新型机器学习方法，以及致力于尖端显微镜技术的生物工程师。他们的雄心是开发新一代、受物理约束的人工智能，它可以比目前的系统成像更好、更快、更智能。通过在人工智能的设计中嵌入物理约束，您将开发出更好的显微镜和生物成像技术，这些技术将在新一代荧光显微镜和医疗监控设备上进行测试。这项研究将在格拉斯哥的高级研究中心(ARC)进行，在那里你将与一个由物理学家、计算科学家、工程师和生物学家组成的团队合作。该项目将与DotPhoton(瑞士)密切合作，理想情况下还将包括亲自访问公司办公场所，与他们的数据科学家团队合作。

Physics-informed and physics-constrained machine learning for next generation imaging.A team of researchers from the University of Glasgow and the "big image data" company, Dotphoton. The student will be part of a multidisciplinary team of physicists and working on novel imaging techniques, of computer scientists working on novel machine learning approaches that encode the physics of the imaging problem and bio-engineers working with cutting edge microscopy techniques. The ambition is to develop new-generation, physics-constrained AI that can image better, faster and more intelligently that current systems. By embedding physical constraints in to the design of the AI, you will develop better microscopes and biological imaging techniques that will be tested on new-generation fluorescence microscopes and healthcare monitoring devices. The research will be carried out at the Advanced Research Centre (ARC) in Glasgow, where you will work with a team of physicists, computing scientists, engineers and biologists. The project will be in close collaboration with Dotphoton (Switzerland) and will ideally involve also in-person visits to the company premises to work with their team of data scientists.