Internship at the Institute of Mathematics of the CAS about Data Fitting (approximating data with a curve or a surface), which is all around us — from engineering applications and finance to medicine and machine learning. As the amount of available data keeps growing, the computational demands become more and more challenging. This is where numerical analysis comes in, developing new algorithms and methods to handle these problems efficiently.
Many practical data fitting problems can be formulated as least squares problems — a classical and well-studied mathematical topic. However, solving these problems for large-scale datasets is still an active area of research, and implementing truly efficient algorithms is far from trivial.
High-quality test data are essential for validating and comparing numerical methods. And this is exactly where you come in. In numerical analysis, publicly available datasets are used only rarely — terminology differs across fields, datasets are often hard to find, and they are frequently stored in non-standard formats or tied to specific software.
What you will do during the internship:
- Explore the “terminology jungle” and identify applications related to least squares problems
- Collect publicly available datasets and convert them into a unified, easy-to-use format
- Prepare datasets for tools commonly used in numerical analysis — Python, MATLAB, Julia
Who we are looking for:
- A curious person who enjoys discovering new things
- Basic programming skills (ideally Python)
- Ability to work with English resources
What you can gain:
- Hands-on experience with real research problems
- Deeper insight into numerical analysis and data processing
- A publicly available dataset package you can showcase in your CV or LinkedIn
- A meaningful output that can support future research