Bosch, PhD in Generative Models to Improve Out-of-distribution Generalization, Industry-on-Campus Lab (University of Tübingen)

  • Publication date
  • Closing date
  • Level of education
  • Salary indication

06-07-2021

30-09-2021

Master

According to Germany salary scale 13 TV-L

Apply now!

The Bosch Industry-on-Campus Lab, a research collaboration between the University of Tübingen and Bosch Center for AI (BCAI), invites applications for an open PhD position in Generative Models to Improve Out-of-distribution Generalization. 

The aim of this project is to improve the out-of-distribution generalization of machine learning systems. The assumption that the training and test data of machine learning systems are independently sampled from the same distribution does not necessarily hold in practice. Being able to trust model predictions at test time, the requirement on model’s generalization shall not only concern in-distribution behind the training data but also out-of-distributions beyond it. To this end, you will specifically investigate deep generative models, exploiting their use for data manipulation, data generation recovery, representation learning, and density estimation.

You will be jointly supervised by Dr. Dan Zhang from the Bosch side and by Prof. Andreas Geiger from the University of Tübingen side.

The position is available immediately (but start date is negotiable), the contract is initially for three years, and remunerated according to the German salary scale 13 TVL.

About us

Roles & responsibilities

As part of the Bosch Industry-on-Campus Lab at the University of Tübingen, you are going to carry out AI research and develop novel machine learning methods for safety critical applications in autonomous driving, healthcare, and manufacturing systems. There will also be regular visits and interactions with researchers at Bosch Center for AI, who have an office on campus. At the University of Tübingen you will be supervised by Dr. Dan Zhang and Prof. Dr. Andreas Geiger.

Your tasks will be to:

Develop new generative methods to improve the out-of-distribution generalization of machine learning models;

Collaborate with other researchers within the lab and BCAI Research;

Complete and defend a PhD thesis within the official appointment duration of three years;

Regularly present internally on your progress and help Bosch write patent applications to protect inventions from the lab when requested;

Regularly present intermediate research results at international conferences and workshops, and publish them in proceedings and journals;

Potentially assist in relevant teaching activities.

Master’s degree in Computer Science, Artificial Intelligence, Mathematics, or related field;

Strong background in machine learning and/or computer vision; 

Excellent programming skills, preferably in Python;

Prior experience of working with deep learning libraries, such as PyTorch or TensorFlow;

Solid mathematics foundations, especially in probability theory, statistics, calculus and linear algebra;

High motivation and creativity;

Strong communication, presentation and writing skills and excellent command of English.

Prior publications in relevant machine learning and vision venues as well as experience working with deep generative models will be advantageous for your application.

Requirements

We offer

You will have an opportunity to work jointly with the Tuebingen academia talent and the industrial Bosch research team on challenging topics to create real world impact. 

Tübingen has an internationally renowned research community in artificial intelligence, machine learning and computational neuroscience, including the Cyber Valley Initiative, the Tübingen AI Center, and the Excellence Cluster Machine Learning. We are situated in the AI Research Building, in close proximity to the Max Planck Institutes for Intelligent Systems and Biological Cybernetics. Tübingen is a scenic university town on the Neckar river in South-Western Germany. The quality of life is exceptionally high and the atmosphere is diverse, inclusive, and most locals speak English. You can find out more about Tübingen here: https://www.tuebingen.de/en/