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

Your mission:
You will

• work on machine learning research projects, in particular addressing the question of how machine learning methodologies can be further improved in order to achieve better performances in relevant applications; those applications are likely to be connected to either computer vision or natural language processing, however, also more general methodological questions might be of interest (e.g. meta learning, hyperparameter search, efficient training of neural networks)

• also work on transferring theoretical insights to practical use cases which are usually part of respective research projects, with the goal of setting new industry standards

• be able to publish your results in relevant journals or at machine learning conferences such as ICML, NeurIPS or ICLR

• foster our strong connections to academia, keep up to date with advances in machine learning research and actively take part in our company’s collaborative learning and development environment

• support the dida team in applying cutting-edge machine learning algorithms

Your Profile:
You have
• a MSc or PhD in mathematics, physic, computer science or a related field
• published academic papers; machine learning related papers are a plus
• a creative mind that likes to solve problems
• grounded knowledge about machine learning and deep learning in particular

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 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.