Corsmed is an early-stage tech-oriented startup, developing the simulation engine and the reconstruction framework for the next-generation medical imaging software. As such, our team works at the intersection of applied physics, mathematics, numerics, software and biomedical engineering.
We are hiring at all career stages, and we are looking for you who want to grow within the company, taking a role in future leading positions. We value our culture, and seek people who are ambitious and professional, but still humble and friendly.
Our teams are distributed across Europe, so you should have excellent English communication skills and can work both independently and in a team.
As part of a young and fast-paced startup, you should be curious and self-motivated, able to tackle complex problems, be willing to step out of your comfort zone and learn and work on new technologies and topics.
You should also have experience with most of the following technologies:
- C/C++, CUDA
- Python, Matlab
- Torch, Tensor Flow
- Build a solid understanding of the inverse problems associated to medical imaging
- Develop the algorithms and build a general optimization framework
- Build automated solutions for large-scale, patient-facing, clinical problems
- Communicate clearly and work closely with manager and technical leads to refine solutions and to describe changes that may affect others
- Deliver state-of-the art specification documents that meet requirements on schedule Ensure that specs are efficient, maintainable, extensible, robust and easy to understand
- Expand depth and breadth of knowledge in specific areas of signal/image processing and mathematical optimization
- BS degree in Electrical Engineering, Computer Science, Applied Math or related field, with two years of experience or MS in the above fields
- Advanced signal/image processing techniques related to Iterative reconstruction methods for inverse problems (primal-dual optimization, augmented Lagrangian, etc.)
- Large-scale mathematical optimization (convex / non-convex, stochastic optimization)
- Ability to learn quickly, understand complex systems and work closely with others
- Ability to complete high-quality work on time
- Ability to work independently and with a minimum of supervision
- PhD in Electrical Engineering, Computer Science, Applied Math or related field
- Demonstrated skills in the following areas are a plus:
- MR image reconstruction
- Supervised learning methods (linear and logistic regression, generalized linear models, support vector machines, graphical models, convolutional and recurrent neural networks / deep learning, etc.)
- Numerical Linear Algebra