Machine Learning Scientist (all genders) Hybrid, Berlin
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Posted on:
Yesterday
Vacancies:
1 Vacancy
Job Summary
Your mission
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
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 companys 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 experiences (Python is a bonus)
- 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 Kreuzberg close to Moritzplatz. 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.
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.
About us
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.
At dida we stand 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.
At dida we stand 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.
Required Experience:
IC
About Company
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 th ... View more
