STUDENT ASSISTANT - COMPUTER VISION RESEACHER ON SPORTS DATA ANALYSIS (M/W/D)

fortiss is the state research institute of the Free State of Bavaria for the development of software-intensive systems, based in Munich. The scientists at the institute work in research, development and transfer projects with universities, research institutions and technology leaders in Bavaria, Germany and Europe. They research and develop methods, techniques and tools for reliable, secure and comprehensible software solutions and artificial intelligence applications. Fortiss is organised in the legal form of a non-profit limited liability company. The shareholders are the Free State of Bavaria (majority shareholder) and the Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. (Fraunhofer Society for the Promotion of Applied Research).    To strengthen our Machine Learning team, we are looking for a Computer Vision Researcher on Sports Data Analysis - Working Student (m/w/d)   Who We Are and How We Work:   · We exchange ideas on projects and new tasks in weekly meetings. · We always keep our common goal in mind and support each other in achieving it. · Our cooperation is characterized by flat hierarchies and teamwork. · We always have an open ear for new ideas, and we tackle new challenges together. · Enthusiasm for scientific work and research projects invites us to exchange ideas.

· Investigate action recognition algorithms, adapting the most suitable approach for our custom unlabeled data. · Develop and fine-tune computer vision models using MMLab pipelines, such as MMAction2 and MMPose, particularly for action recognition based on pose estimation if applicable. · Customize MMLab functionalities and entry points to align with specific requirements of the research team. · Conduct theoretical and applied research into semantic disentanglement to improve the self-supervised learning results of action classification.

· Experience with object-oriented programming. · Hands-on experience with MMLab pipeline families, such as MMDet, MMPose, MMAction2, and MMYOLO. · Knowledge in computer vision tasks such as object detection, pose estimation, and action recognition. · Solid understanding of fundamental mathematics and machine learning, with the ability to read and comprehend research papers. Familiarity with self-supervised learning is preferred, as the work involves unlabeled custom data. · Completion of a bachelor's degree and current enrollment in a master's degree program in electrical engineering, computer science, information systems, or a related field. · Excellent communication skills in both spoken and written English.

· Six-month part-time on-site working student contract, expected to start in March. However, we are open to negotiation and can adapt to individual candidates on a case-by-case basis.  · An international and dynamic work environment surrounded by highly qualified colleagues. · Opportunities to gain experience with the latest developments in deep learning and numerous avenues for professional and personal growth. · Exposure to industry work and research, providing valuable insights into real-world applications.

Then we look forward to receiving your complete application with curriculum vitae and current transcript. Please note that responses may be delayed due to the Christmas and New Year holidays.

Job-ID: ML-SH-06-2024
Contact: Tianming Qiu