— Good understanding of state-of-the-art and main trends in machine learning
— Ability to do research independently, from literature review to prototype validation
— Solid background in machine learning, including deep learning (3+ years),
— Commercial experience with image processing applications (classification, detection, segmentation etc.)
— Proficiency in Python.
— Practical usage of deep learning frameworks in real projects (preferably TensorFlow/PyTorch)
— Strong mathematical and analytical skills
— Knowledge of basic data structures and algorithms
— Master degree in a relevant field (e.g., Machine Learning, Applied Mathematics, Computer Science, Engineering)
— Upper intermediate level of English
— Hands-on experience in image processing
— Knowledge of medical imaging domain
— C++ programming skills
— GPU computing
— Build local & international career
— Contribute in full cycle of product development
— Be a part of future technologies
— Use Learning and development system
— Learn & use English
— Trip to Europe
— You will be a Research Engineer who works on a cross-section of three domains: medical image processing, 3D printing and machine learning. You will be challenged with interesting non-trivial problems and your solutions will have real impact on people’s health.
— You will work on topics similar to the next ones: automatic object detection/segmentation, landmark detection, statistical shape modelling, automatic surgery planning, interactive image segmentation, multimodal image registration, 2D/3D reconstruction, image denoising etc.
— You analyze the problem, clarify requirements, make literature review, prototype, implement, validate, document and support integration of your solutions into the final desktop or cloud solution. You have quite some freedom in doing research as long as you can explain your decisions to your teammates and all requirements are met.
— You are aware of recent relevant academic and industrial achievements and look for opportunities to apply them in our domains.
— You consult other colleagues on machine learning topics.