Research Assistant - salary grade E13 TV-L Berliner Hochschulen - For qualification

The tasks include implementing XAI-enabled ML models to identify patterns and predictors for heart failure, developing a prototype of an infrastructure for model use by clinical users, and conducting clinical validations with independent data sets to test the generalizability and robustness of the AI models. Teaching Tasks.

• Successfully completed university studies (Master, Diplom or equivalent) in mathematics, physics, computer science or bioinformatics
• Several years of experience in the field of statistical methods and machine learning, preferably in the field of neural networks and explainable AI
• Very good programming skills (e.g. in Python, NumPy/SciPy, PyTorch/TensorFlow) are essential
• Experience in the analysis of medical data, including analysis of data from large biobanks, is required - 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, quoting the reference number and including the usual application documents (i.e. at least cover letter, CV, graduation certificates, grade overviews, etc.), to Technische Universität Berlin - Die Präsidentin - Fakultät IV, Institut für Softwaretechnik und Theoretische Informatik, FG Maschinelles Lernen, NWG Prof. Dr. Grégoire Montavon, Sekr. MAR 4-1, Marchstr. 23, 10587 Berlin or by email (one PDF file, max. 5 MB) at: jobs@bifold.berlin.

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

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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. Applications from people of all nationalities and with a migration background are very welcome.

Technische Universität Berlin - Die Präsidentin - Fakultät IV, Institut für Softwaretechnik und Theoretische Informatik, FG Maschinelles Lernen, NWG Prof. Dr. Grégoire Montavon, Sekr. MAR 4-1, Marchstr. 23, 10587 Berlin