• Independent and autonomous research in the field of Machine Learning with a focus on unsupervised learning and explainability
• Management, coordination and acquisition of third-party funded projects
• Further development of learning theory
• Theory and practical application of ML methods in anomaly detection clustering and unsupervised data analysis
• Development of robust and interpretable models to avoid misleading patterns (e.g. spurious correlations)
• Applications to Big Data in the sciences (e.g., in natural sciences, life sciences, and digital humanities)
• Teaching responsibilities: Independent organisation and realisation of courses and their respective exams

• Successfully completed scientific university degree (Master's, Diploma or equivalent) and doctorate in computer science, mathematics or physics
• After completing the university degree programme, at least three years of academic or practical work experience in a full-time employment relationship
• Several years of experience as a research assistant in the area of Intelligent Data Analysis and Machine Learning
• Teaching and supervisory experience in the field of Machine Learning and related areas
• The ability to teach in German and/or in English is required; willingness to acquire the respective missing language skills
• Extensive, in-depth knowledge of the following fields:
  o Learning theory
  o ML-based anomaly detection, clustering and data analysis
  o Development of robust and interpretable models addressing model reliability and validity
  o Application of ML in the sciences
• Experience in publishing in high-ranking international journals is an advantage
• Experience in international scientific collaboration, e.g., through research stays abroad is an advantage
• Experience in developing and refining teaching materials and innovative educational concepts is desirable
• Willingness to contribute to academic service and administration and management of scientific operations is desired

Please send your application by e-mail to Prof. Dr Klaus-Robert Müller (sekr@ml.tu-berlin.de), quoting the reference number, together with the usual application documents (cover letter, CV, certificates, letters of recommendation - one PDF file, max. 5 MB).

By submitting your application via email you consent to having your data electronically processed and saved. Please note that we do not provide a guaranty for the protection of your personal data when submitted as unprotected file. Please find our data protection notice acc. DSGVO (General Data Protection Regulation) at the TU staff department homepage: https://www.abt2-t.tu-berlin.de/menue/themen_a_z/datenschutzerklaerung/ .

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.