Satyasaran Changdar
Postdoc
Ingredient and Dairy Technology
Rolighedsvej 26
1958 Frederiksberg C
I am [Satyasaran Changdar](https://di.ku.dk/english/staff/vip/?pure=en/persons/723168), Postdoc, working in modelling using Scientific Machine Learning in project 'SmartClean',Ingredient and Dairy Technology, Department of [Food Science](https://food.ku.dk/english/) [University of Copenhagen] (https://www.ku.dk/english/) under the Supervisons of Prof.[Serafim Bakalis](https://food.ku.dk/english/staff/?pure=en%2Fpersons%2Fserafim-bakalis(1a4bf354-3180-4450-9834-3cd052164d3c)%2Fpublications.html) and collaborted with [Arla foods](https://www.arla.com).
Earlier I worked [July 2021-Feb 2024] as a postdoc in Applied Machine learning in Plant physiology under the supervision
I am Satyasaran Changdar, Postdoc (joined in July 2021) in Machine Learning at the University of Copenhagen. I am under the Supervisons of Professor Erik Bjørnager Dam, Department of Computer Science, University of Copenhagen and Professor Kristian Thorup-Kristensen, Department of Plant and Environmental Sciences, University of Copenhagen, Denmark and completed my Ph.D. in applied mathematics in 2019 at University of Calcutta, master of technology in Computer Applications from Indian Institute of Technology, Delhi, India in 2008 and masters in mathematics at Indian Institute of Technology, Bombay, India in 2005. Prior to joining Postdoc, I worked as an associate professor at the institute of Institute of engineering and Management, Kolkata, India.
My research lies in Machine Learning, Deep Learning for sub-soil root image and 3D MRI images analysis, Physics-informed Neural Networks(PINNs) for solving non-linear Partial differential equations, and Data-driven Scientific Computing. I have been actively involved in the development of machine learning methods that learn complex patterns from multimodal agriculture data in crop science from RadiMax in RadiBooster project.
For more information please visit my site: https://satyasaran.github.io/
ID: 283968692
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Non-invasive phenotyping for water and nitrogen uptake by deep roots explored using machine learning
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