Mathematician, Physicist (w/m/d) - Hybrid

Permanent employee, Full or part-time · Berlin

Your mission

dida is a machine learning software company working on exciting problems in computer vision and natural language processing. Our team addresses applied problems for various clients using the latest scientific advancements, especially in deep learning, believing that research-oriented thinking enhances the efficiency of solving real-world problems.

Your responsibilities

1. Support the dida team in applying cutting-edge machine learning algorithms.
2. Experiment with model architectures to find optimal solutions for each project.
3. Stay updated with advances in machine learning research and participate in our collaborative learning environment.

Your profile

You have:

1. A MSc or PhD in mathematics or physics.
2. A creative mindset eager to solve problems.
3. An interest in modern machine learning approaches (experience in deep learning is a plus).
4. Solid programming experience (Python is a plus).

Required application documents

• CV (mandatory)
• (University) Degree(s) (mandatory)
• Transcripts (if applicable)
• Project / Code Examples / Portfolio (optional)

Why us?

You will work with an interdisciplinary team with strong backgrounds in mathematics and statistics. We offer flexible working hours (full or part-time) and a nice office with good coffee in Berlin Schöneberg. While we prefer a hybrid work model, remote work is also an option. We support publishing your research results.

Current projects

1. Estimate the number of solar panels on a roof (computer vision): Using satellite and ground images, automatically detect roof elements to determine how many solar panels can fit, inferring 3D information from 2D images.
2. Legal text analysis (NLP): Automate the review of legal documents to classify and assess the legal effectiveness of paragraphs, involving text extraction, labeling schemes, and automatic paragraph detection.

dida values diversity and encourages applications from women, people of color, and individuals with disabilities, regardless of gender, nationality, ethnicity, or disability.

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