ELMI was created in 2001 to establish a unique communication network between European scientists working in the field of light microscopy and the manufacturers of their equipment.
The FMI seeks an enthusiastic and highly motivated individual to drive the development of machine learning tools for restoration, segmentation and classification of biomedical image data.
The candidate will be responsible for the development and application of machine learning tools, in particular convolutional neural networks for the analysis of a broad spectrum of image data. Research groups at the FMI use a variety of imaging methods to address questions in cell biology, genetics, and neurobiology. Tasks to be solved include, but are not limited to, image denoising, segmentation, object tracking and classification. The candidate will be part of the Facility for Advanced Imaging and Microscopy (FAIM) and will be supported by the IT group for high-performance computing. In this position, you will also teach image processing to students and post-docs and help maintain the computational infrastructure.
We are looking for a highly motivated and skilled individual with expertise in machine learning and computer vision. The candidate should have a strong background in machine learning theory as well as practical experience with developing and deploying state-of-the-art machine learning methods for image analysis. Experience with toolboxes such as TensorFlow or PyTorch, as well as a master’s degree in computer science, applied mathematics, physics or an appropriate engineering discipline are required. Experience with biological microscopy would be beneficial.
The ideal candidate is fluent in multiple computer programming languages and can easily adapt to heterogeneous computing environments.
For additional information please check the job offer.
Closing date 17 January 2021.