Machine Learning Working Student


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Your mission

You will
  • meet an interdisciplinary team of people with a solid background in mathematics and statistics
  • have flexible working hours and a nice office with good coffee in Berlin Schöneberg (of course, you can work mostly remote during Corona)
  • work on exciting NLP and computer vision tasks, often involving deep learning
  • be part of a project team of 3 experienced machine learning scientists, who will ensure that you learn a lot
  • get 12 Eur/h

Your profile

You are
  • studying mathematics, physics, computer science or something similar
  • interested in applied statistics and in programming with Python, Julia or something alike
  • eager to learn more about modern machine learning approaches such as deep learning
  • planning to stay in Berlin at least 1.5 more years and to work 20h/week
  • interested in solving real-world problems with creative ideas

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.

Why us?

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 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 modelling), and detecting different paragraphs automatically, before tackling the inference task.