Machine Learning Working Student (all genders) - Hybrid, Berlin

dida is a machine learning software company with exciting problems for instance in computer vision and natural language processing. Our team tackles applied problems for different customers by using latest scientific advancements (especially in deep learning) and therefore believes that research oriented thinking can help solving real-world problems more efficiently. And for this we need a Machine Learning Working Student, who will:

• support our interdisciplinary team of people, who have a solid background in mathematics and statistics 
• work on an interesting machine learning tasks, ranging from research projects to industry products
• shape and develop our projects and products, together with our machine learning experts

• You get the opportunity to grow professionally with us and quickly assume significant responsibilities.
• You will work with and learn from people who believe in knowledge sharing and treat each other very sensitively.
• We are in an exciting phase where you can actively contribute to shaping the development of dida. With us, you will have the chance to take on responsibility and make your mark.
• You will have flexible, hybrid working hours and a nice office with good coffee in Berlin Schöneberg.

We have maintained our curiosity from day one, which means that we want to learn and explore new things. If you want to learn more about our projects, here you find two examples. You can find more here:

Estimate the amount of solar panels that fit on a roof (computer vision):
Given a satellite picture and a ground image of a house, automatically detect certain elements of a roof (including obstacles, dormers etc.) in order to find out how many solar panels fit on it. This involves inferring 3d information from 2d pictures in order to infer the roof pitch.
Detect, classify and suggest legal effectiveness of text paragraphs (NLP):
Automatically go through thousands of legal documents with the goal to classify dedicated paragraphs and check their legal effectiveness. This involves converting scans to text, coming up with a labelling scheme (problem modelling), and detecting different paragraphs automatically, before tackling the inference task.

Please attach the following documents:

• CV (mandatory)
• (Uni) Degree(s) (mandatory)
• Transcripts (where applicable)
• Project / Code Examples / Portfolio (optional) - what helps us, in getting to know you better