Research Assistant - salary grade E13 TV-L Berliner Hochschulen - 1st qualification period (PhD candidate)

Research in the field of machine learning (ML); development of novel learning methods and models that lead to robust, latent representations of images and thus to generalization to data that do not come from the training distribution (i.e., out-of-distribution), especially in cases of data scarcity and unequal distribution of classes in medical areas such as oncology; design and optimization of variational methods of compression and interpretation of continuous coordinate spaces; software implementations in all of these areas. Teaching tasks.

• Successfully completed university degree (MSc, Diploma or equivalent) in Computer Science, Physics, Applied Mathematics or relevant field
• Solid knowledge of linear algebra, statistics and machine learning, especially neural networks, deep generative models, matrix factorization and interpretation methods as well as variational inference
• Practical experience in the development and application of machine learning algorithms
• Solid programming skills, especially experience with ML, bash scripting, and linear algebra libraries (PyTorch, JAX, TensorFlow, NumPy, etc.)
• The ability to teach in German and/or in English is required; willingness to acquire the respective missing language skills

Please send your written application, stating the reference number, with the usual application documents to the Technical University of Berlin - The President - Faculty IV, Institute for Software Technology and Theoretical Computer Science, Machine Learning Group, Prof. Dr. Müller, MAR 4-1, Marchstr. 23, 10587 Berlin or by e-mail (a PDF file, max. 5 MB) to: jobs@bifold.berlin.

For cost reasons, application documents sent by mail will not be returned. Please submit copies only.

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To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities. pplications from people of all nationalities and with a migration background are very welcome.