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PhD Researcher in Uncertainty-Aware Deep Learning for Parametric PDEs Modeling Tumor Growth
hace 2 meses
Research Topic: Uncertainty-Aware Deep Learning for Parametric PDEs Modeling Tumor Growth
About the Project:
The successful candidate will be part of a research project focused on developing uncertainty-aware deep learning methods for parametric partial differential equations (PDEs) modeling tumor growth. The goal is to create a physics-informed neural network that can quantify and reduce uncertainty in tumor growth predictions.
Key Responsibilities:
- Develop and implement uncertainty-aware deep learning methods for parametric PDEs
- Design and conduct experiments to evaluate the performance of the proposed methods
- Collaborate with the research team to integrate the developed methods into a comprehensive tumor growth modeling framework
- Contribute to the development of research papers and presentations
Requirements:
- PhD in Computer Science, Mathematics, Physics, or a related field
- Strong background in deep learning, PDEs, and uncertainty quantification
- Excellent programming skills in Python or C++
- Experience with neural network libraries such as TensorFlow or PyTorch
- Good communication and collaboration skills
What We Offer:
- A dynamic and interdisciplinary research environment
- The opportunity to work on a cutting-edge research project
- A competitive salary and benefits package
- The chance to collaborate with a team of experienced researchers
