Informatiker/in (Hochschule)

As part of the German government's artificial intelligence (AI) strategy, the successful Saxon competence center ScaDS.AI Dresden/Leipzig (Center for Scalable Data Analytics and Artificial Intelligence) is being expanded into a leading German AI competence center for Big Data and artificial intelligence (AI). For TUD Dresden University of Technology diversity is an essential feature and a quality criterion of an excellent university. Accordingly, we welcome all applicants who would like to commit themselves, their achievements and productivity to the success of the whole institution.

At the Center for Interdisciplinary Digital Sciences (CIDS), the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI Dresden) offers a position as

Research Associate / Postdoc (m/f/x) (subject to personal qualification employees are remunerated according to salary group E 14 TV-L)

starting at the earliest possible date. The position is limited until April 30, 2027 with the option of extension. The period of employment is governed by the Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz - WissZeitVG).

Professional assignment: Chair of Scalable Software Architectures for Data Analytics (Prof. Dr. Michael Färber)

Research area: Natural Language Processing (Large Language Models), Machine Learning, Knowledge Graphs

Tasks:

• research with a high degree of autonomy in complex areas such as Natural Language Processing, Knowledge Graphs and Machine Learning (e.g. Integrating Knowledge Graphs in Language Models)
• willingness to explore scientifically complex and daily changing AI topics, such as the scalability of Large Language Models
• co-supervision of PhD students
• creating project proposals and operational project management if necessary
• collaboration in national and international research projects, potentially with industrial connections, including the responsible management of the work packages
• presentation of research results at leading international conferences.

The position comes with access to high performance computing resources and access to training opportunities within ScaDS.AI.

Requirements:

• excellent university and PhD degree in Computer Science, Data Science, Machine Learning, Computational Linguistics, Mathematics, or a related field
• a proven publication track record in areas such as Natural Language Processing (e.g., LLMs), Knowledge Graphs, Machine Learning (e.g., GNNs) or Recommender Systems
• passion for scientific publications and applying for research projects
• very good written and spoken English skills.

TUD strives to employ more women in academia and research. We therefore expressly encourage women to apply. The University is a certified family-friendly university and offers a Dual Career Service. We welcome applications from candidates with disabilities. If multiple candidates prove to be equally qualified, those with disabilities or with equivalent status pursuant to the German Social Code IX (SGB IX) will receive priority for employment.

Please submit your detailed application with the usual documents (Cover letter, CV, copies of your references and certificates), quoting the job number „ScaDS.AI Färber2”, by September 6, 2024 (stamped arrival date of the university central mail service or the time stamp on the email server of TUD applies) to: TU Dresden, ScaDS.AI, Herrn Prof. Dr.-Ing. Michael Färber, Helmholtzstr. 10, 01069 Dresden, Germany or via the TUD SecureMail Portal https://securemail.tu-dresden.de by sending it as a**** single pdf file to scads.ai@tu-dresden.de. Please submit copies only, as your application will not be returned to you. Expenses incurred in attending interviews can be reimbursed.

Reference to data protection: Your data protection rights, the purpose for which your data will be processed, as well as further information about data protection is available to you on the website:**** https://tu-dresden.de/karriere/datenschutzhinweis.

Unsere Stellenausschreibungen richten sich stets an alle berufsfähigen Menschen, unabhängig von Alter, Geschlecht, Herkunft, sexueller Orientierung, Behinderung, Religion und Weltanschauung etc. Die Bewerberauswahl erfolgt ausschließlich qualifikationsorientiert. Irrtümer in den Angaben und der Schreibweise vorbehalten.