Mathematician, Physicist

Permanent employee, Full or part-time · Berlin

Your mission

dida is a machine learning software company with exciting problems in computer vision and natural language processing. Our team tackles applied problems for different customers by using the latest scientific advancements (especially in deep learning) and believes that research-oriented thinking can help solve 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.

Your Profile

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 experience (Python is a bonus)

Minimum type of documents to attach:

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

Why us?

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 publishing your research results.

Find below a short description of two of our current projects:

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 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 labeling scheme (problem modeling), and detecting different paragraphs automatically before tackling the inference task.

dida stands for equal opportunities, regardless of gender, nationality, ethnic background, or disability. We encourage everyone, especially women, people of color, and people with disabilities, to apply at dida.

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