STUDENT ASSISTANT – COMPUTER VISION RESEACHER ON SPORTS DATA ANALYSIS (M/W/D)
- 80805 München, Guerickestr. 25, fortiss GmbH
- Working student
- Part time
Who are we?
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.
Your Tasks:
· 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.
Your profile:
· 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.
Our offer:
· 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.
Did we catch your interest?
Job-ID: ML-SH-06-2024
Contact: Tianming Qiu
- Ralf Kohlenhuber
- Human Resources Administrator
- Tianming Qiu
- Scientific Employee