Machine Learning Scientist (w/m/d) - Hybrid

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.

You will

• support the dida team in applying cutting-edge machine learning algorithms
• experiment with model architectures to find the best solution for each specific project
• keep up to date with advances in machine learning research and actively take part in our company’s collaborative learning and development environment.

You have

• a MSc or PhD in mathematics or physics
• a creative mind that likes to solve problems
• interest in modern machine learning approaches (experience in deep learning is a bonus)
• solid programming experiences (Python is a bonus)

Minimum type of documents to attach:

• CV (mandatory)
• (Uni) Degree(s) (mandatory)
• Transcripts (where applicable)
• Project / Code Examples / Portfolio (optional)

You will work with an interdisciplinary team of people with a solid background in mathematics and statistics. We offer flexible working hours (full and part time) and have a nice office with good coffee in Berlin Schöneberg. We prefer a hybrid work model, but are open to remote work. We believe in science and support with publishing your research results.
Here are short descriptions of some projects we have worked on:

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 modeling), and detecting different paragraphs automatically, before tackling the inference task.