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Technische Universität Dresden - Center for Interdisciplinary Digital Sciences (CIDS), Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI Dresden)

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.

Student Assistant (m/f/x) (max. 19h/week)

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

Student Assistant (m/f/x) (max. 19h/week)

starting as soon as possible and is limited to 12 months with the option of extension. The period of employment is governed by the Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz – WissZeitVG) as well as the Higher Education Act in the Free State of Saxony (Sächsisches Hochschulgesetz – SächsHSG) in conjunction with the TdL guidelines (collective bargaining association for the German federal states) for Student Assistants and Research Assistants dated February 28, 2024.
We are looking for students to join us in the research on Natural Language Processing (NLP), Large Language Models (LLM), Knowledge Graphs (KG), and Graph-based Machine Learning. The successful candidate will have the opportunity to contribute and collaborate with top AI researchers on cutting-edge research projects, including co-publishing the results at top AI journals/conferences. The research can also lay the ground for the candidate’s Master's thesis.

Working field:

academic support, esp. (including implementation and evaluation) on the following research topics, but not limited to:

  • Support in investigate and develop methods to integrate LLMs with KGs, for example for a highly accurate and explainable recommendation system.
  • Assistance in investigate and develop methods on mitigating the limitations of LLMs, such as hallucination, bias, and inaccurate source citation.
  • Assistance in investigate and develop algorithms for various Graph-based Machine Learning tasks, including link prediction and node classification, along with explanation methods.

Requirements:

  • student enrolled at a college/university,
  • solid Python programming skills and familiarity with at least one deep learning framework (such as PyTorch, TensorFlow, or Keras),
  • a strong interest in Machine Learning, Natural Language Processing, or Graph Theory, coupled with an eagerness to learn the state-of-the-art technologies in these domains.

How to apply:

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 SHK Prof. Färber” by July 18, 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. 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 cannot 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.