City:  Berlin
Date:  Apr 20, 2024

Student Assistant Machine Learning, Remote Sensing & Computer Vision

The Fraunhofer-Gesellschaft (www.fraunhofer.com) currently operates 76 institutes and research institutions throughout Germany and is the world’s leading applied research organization. Around 30 800 employees work with an annual research budget of 3.0 billion euros. 

Future. Discover. Together. 
The Interactive & Cognitive Systems (ICS) group within the Vision & Imaging Technologies (VIT) department is on the lookout for a student assistant with a strong focus on Machine Learning and Computer Vision for remote sensing and processing satellite data. Our group studies natural and designs artificial cognitive systems, researches their relationships and develops methods that enable interactions between them. The group's research is at the interface between cognitive science, machine learning and human-machine interaction. The applications of the developed solutions extend over a wide field of multimedia technology and augmented reality, through medicine to agriculture and industrial production. Your expertise and passion for machine learning and computer vision, particularly in the context of remote sensing, will be invaluable. By joining our team, you will have the opportunity to work on cutting-edge projects, applying your skills to develop algorithms and models that can extract meaningful information from satellite imagery. Become a part of our team and join us on our journey of research and innovation!

 

What you will do

  • Conduct comprehensive research and apply the latest ML and CV algorithms to Remote Sensing data

  • Develop and implement models for the analysis and interpretation of satellite imagery, focusing on environmental, agricultural, or urban application areas

  • Collaborate with the team to manage and optimize datasets derived from satellite sources, ensuring their effective use in model training and evaluation

 

What you bring to the table

Enthusiasm for working collaboratively within an interdisciplinary team, pushing the boundaries in remote sensing, ML, and CV

 

Desirable:

  • Proficiency in programming languages and tools relevant to data science and AI, such as Python, OpenCV, and PyTorch

  • Experience with remote sensing technologies, GIS software, satellite image processing techniques, and analyzing geospatial data (is a plus) 

  • Student status for at least the next 12 months

  • A completed bachelor's degree in computer science or related field, or equivalent knowledge. Candidates nearing the completion of their bachelor's program are also welcome to apply.

 

What you can expect

  • Fascinating challenges in a scientific and entrepreneurial setting

  • Attractive salary

  • Modern  and excellently equipped workspace in central location

  • Great and cooperative working atmosphere in an international team

  • Opportunities to write a master's thesis

  • Flexible working hours

  • Opportunities to work from home (up to 35% of working hours)

 

The position is initially limited to 6 months. An extension is explicitly desired.

 

The monthly working time is 80 hours. This position is also available on a part-time basis. We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. 

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future. 

Interested? Apply online now. We look forward to getting to know you!
 

Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute HHI 

www.hhi.fraunhofer.de 

 

Requisition Number: 72617                Application Deadline: 04/30/2024

 


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