Researcher - Natural Language Processing
dida Datenschmiede
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 companys collaborative learning and development environment
support the dida team in applying cutting-edge machine learning algorithms
a MSc or PhD in mathematics physic computer science or a related field
machine learning related papers are a plus
grounded knowledge about machine learning and deep learning in particular
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. Detect classify and suggest legal effectiveness of text paragraphs (NLP):
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.
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