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Offer 114 out of 407 from 25/11/22, 07:43

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Tech­ni­sche Uni­ver­sität Ber­lin - Faculty IV - Qua­lity and Usa­bi­lity Lab

Research assistant - salary grade E 13 TV-L Berliner Hochschulen

under the reserve that funds are granted; part-time employment may be possible

The majority of systems and services that are provided by computer science, electrical engineering and information technology finally are oriented on the needs of their human users. To build successfully build such systems and services it essential to investigate and understand users and their behavior when interacting with technology. From this, design principles for human-machine interfaces can be derived and requirements for the underlying technologies can be defined.

The Quality and Usability Lab is part of TU Berlin’s Faculty IV and deals with the design and evaluation of human-machine interaction, in which aspects of human perception, technical systems and the design of interaction are the subject of our research. We focus on self-determined work in an interdisciplinary and international team; for this we offer open and flexible working conditions that promote scientific and personal exchange and are a prerequisite for excellent results.

Working field:

Machine-based fake news detection has already made some progress for text and image data. However, only rudimentary approaches exist for audio, which are being researched and expanded.

Your tasks will be in the field of anonymisation and fake news detection for speech and text media. One focus will be the application and evaluation of methods and large language models (GPT, BERT, etc.), but also methods in the area of data anonymisation of text and audio data will be considered. Here, identities in text and audio files are swapped or distorted. Examples come from the field of disinformation by means of manipulated media such as audio or text interviews from newsrooms and social media channels.

In addition, the applied models are to be extended by three sub-areas: Privacy: Anonymisation and robustness of the models, so that membership inference attacks/adversarial attacks have no chance (especially for the use case of medical data); Fairness: Biases in the data (balanced samples), in the models and also predictions are to be analysed and reduced; and Transparency: Methods from the field of Explainable AI are applied to text and audio data to better understand model predictions.

These extensions will be investigated by means of user studies and optimised in terms of comprehensibility and influence on trust in the AI systems.
The concrete tasks include:
  • Exploring anonymisation methods for text and speech data
  • Scientific examination and further development of methods for anonymisation of medical data as well as for the area of fake news detection
  • Planning and conducting user studies
  • Active participation in the design, construction and evaluation of the overall system
  • Publication and presentation of project and research results in scientific journals, at conferences and workshops

Professionally experienced persons from our team support the self-motivated processing of the tasks in the mentioned areas.

Requirements:

  • Successfully completed university degree (Master, Diplom or equivalent) in computer science, media informatics, media technology or electrical engineering (or similar technical background).
  • Ability to work independently in a team and good self-organisation
  • Good programming knowledge in Python and experience in using it in development environments
  • Experience in the use of machine learning frameworks such as Tensorflow, Keras or PyTorch
  • Sound knowledge of the theory and principles of machine learning
  • Previous experience in the area of Natural Language Processing or speech signal processing
  • Interest in conducting experiments with test subjects to determine quality and user experience
  • Language skills: German and English communication skills
  • Pleasure in the work in an international and interdisciplinary environment
  • Desired prior knowledge (not required):
  • Experience in the efficient preparation of training data for AI-based systems
  • Experience with deep fake or anonymisation methods
  • Experience with transformer-based language models such as BERT or GPT

How to apply:

Please send the following documents, bundled in a single PDF file, to Prof. Dr.-Ing. Sebastian Möller bewerbung@qu.tu-berlin.de:
Letter of application, curriculum vitae, copies of certificates, job references.

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/ 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.

Technische Universität Berlin - Die Präsidentin - Fakultät IV, Quality and Usability Lab, Prof. Dr.-Ing. Sebastian Möller, Sekr. TEL 18, Ernst-Reuter-Platz 7, 10587 Berlin