Technische Universität Berlin
Faculty IV - The Berlin Institute for the Foundations of Learning and Data (BIFOLD)
Technische Universität Berlin offers an open position:
Research Associate (PostDoc) - salary grade E14 TV-L Berliner Hochschulen - For qualification
Part time employment may be possible
The Berlin Institute for the Foundations of Learning and Data (BIFOLD) is one of six national AI centres in Germany and is funded by the State of Berlin and the Federal Ministry of Education and Research. BIFOLD currently consists of 12 research groups with over 150 employees, a graduate school and the BIFOLD office. Fellows from the major Berlin universities, Charité - Universitätsmedizin Berlin and various other national and international universities and non-university research institutions are also involved.
Tasks
The Department of Information Integration and Data Preparation (D2IP) conducts basic and applied research in data integration, data pre-processing and data science. We are currently looking for scientific employees (postdocs) in one or more of the following topics: (1) Research into technologies and systems for detecting data errors in massive data. (2) Research into a scalable method for generating data pre-processing workflows. (3) Research into systems and technologies for efficiently finding data sets in large data lakes.
The employment relationship is related to the regular teaching obligation (§ 5 para. 1 no. 6 or § 5 para. 1 no. 7 LVVO Berlin). Participation in the AI Competence Centre BIFOLD requires a special aptitude for working in research. The tasks at BIFOLD can justify a reduction in teaching duties.
Requirements
Applicants must have a successfully completed university degree (Master, Diplom or equivalent) and a completed PhD specialising in data management, data integration or scalable data analysis, have experience in scientific work as demonstrated by relevant scientific publications, and have solid theoretical and practical knowledge in computer science. In-depth knowledge of database technologies is a prerequisite. The ability to teach in German and/or in English is required; willingness to acquire the respective missing language skills.
Desirable:
You should be interested in developing an innovative system and validating research results in a real-world application environment.Experience in open source development and project management is desirable. Advanced knowledge of machine learning, neural networks and RAG technologies.
How to apply
Please send your application, quoting the reference number, with the usual application documents (i.e. at least cover letter, CV, graduation certificates, grade overviews, list of publications, reference letters etc summarised in a PDF document, no larger than 3 MB) exclusively by e-mail to abedjan@tu-berlin.de.
By submitting your application via email you consent to having your data electronically processed and saved. Please note that we do not provide a guarantee 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/ or quick access 214041.
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, Berlin Institute for the Foundations of Learning and Data – BIFOLD, Ernst-Reuter Platz 7, Sekr.: TEL 9-2, 10587 Berlin
Facts
Published | 18.10.2024 |
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Number of employees | ca. 7000 |
Category | Graduate position, Research assistant |
Location | Germany, Berlin, Berlin, Charlottenburg |
Area of responsibility | Computer science |
Start date (earliest) | Earliest possible |
Duration | for 5 years |
Full/Part-time | full-time; part-time employment may be possible |
Remuneration | Salary grade E14 |
Homepage | http://www.tu-berlin.de |
Requirements
Qualification | Master, Diplom or equivalent and PhD |
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Contact
Reference number | IV-507/24 |
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Contact person | Prof. Dr. Abedjan |
Apply
Application deadline | 15.11.2024 |
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Reference number | IV-507/24 |
By post | Technische Universität Berlin
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By email | abedjan@tu-berlin.de |