IDEAS NCBR Sp. z o.o. is a research and development centre operating in the field of artificial intelligence and digital economy, whose mission is to support the development of these technologies in Poland by creating a platform that connects the academic and business environments.
IDEAS NCBR Sp. z o.o. is a part of the National Center for Research and Development (NCBR Group). Our goal is to build in Poland the largest, friendly to conduct innovative research platform, to educate a new generation of scientists focused on the practical application of the developed algorithms and their subsequent commercialization in the industry, finance, medicine and other branches of the economy.
At IDEAS NCBR, we are constantly on the lookout for new talent. If you are a student or graduate of Applied Mathematics, Computer Science, or a related discipline and would like to pursue a career in research, then share your plans with us by applying for this position. As a team member, you will have the opportunity to work with many authorities in the field of artificial intelligence in the team led by Dr. Przemyslaw Musialski, Assoc. Prof.
With expertise in diverse areas such as geometric modeling, geometry processing, computational fabrication, and machine learning, we strive to create algorithmic solutions for digital content generation. Our research efforts are dedicated to revolutionizing content creation, geometry generation, realistic rendering, and physical simulations in applications ranging from computer games and movie productions to virtual reality and 3D design.
As a part of our group, you will have the opportunity to work on cutting-edge research projects, publish in top-tier conferences and journals, and collaborate with a diverse team of experts.
This PhD research focuses on the development of efficient solvers for partial differential equations (PDEs) using implicit neural networks in the context of so-called Physics Informed Neural Networks (PINN). This project aims to advance the field of physical simulations in computer graphics, with a particular emphasis on applications such as fluid simulation, character animation, crowd simulation, and elastic deformation.
Physical simulations play a crucial role in computer graphics for creating realistic animations, deformations, and interactions within virtual environments. However, traditional numerical solvers for PDEs used in physical simulations can be computationally expensive and challenging to scale for complex scenarios. This Ph.D. project seeks to address these limitations by leveraging Physics Informed Neural Networks, a powerful framework that combines the strengths of neural networks and physics-based modeling.