Johannes Gutenberg University Mainz (JGU) is one of the largest universities in Germany. Thanks to its location in the Rhine-Main science region, the university can unfold to its full potential and showcase its innovative power and dynamism. Its status as a comprehensive university allows for multidisciplinary learning and teaching and has great potential for internationally renowned, interdisciplinary research. Almost all of its institutes are located on a single campus close to the Mainz city center – creating a lively academic culture for researchers, teaching staff, and students from every continent.

The Kann Natural Language Processing (NALA) group headed by Prof. Dr. Katharina Kann at the Institute of Computer Science of Johannes Gutenberg University Mainz aims to fill the following vacancy as soon as possible:

Research Scientist
full-time (100 %)

 
The Institute of Computer Science of JGU is special-ized in data science. The NALA group of Prof. Kann is hosted by both JGU and the University of Colorado Boulder in the USA and is internationally visible (https://nala-cub.github.io). The selected applicant is expected to regularly collaborate with members of the NALA group in Boulder as well as with members of the TOPML project in Mainz.

Your tasks:
The successful applicant will be part of the TOPML (“Trading Off Non-Functional Properties of Machine Learning”) research center at JGU, which is funded by the Carl-Zeiss-Foundation. They will join a highly interdisciplinary team of researchers working on trustworthy AI and studying interactions and depend-encies of different properties of machine learning,
including transparency and fairness of data and algo-rithms, data protection requirements, and the efficient use of resources such as compute. Special consider-ation is also being given to ethical and legal aspects. In the context of TOPML, the successful applicant is expected to work on novel natural language pro-cessing models and algorithms with a focus on one or more of the following:

• multilingual natural language processing and transfer learning;
• natural language processing for low-resource languages;
• natural language processing for domains with limited data (e.g., medicine or education).

Your profile:
Applicants must meet the general requirements
according to public services law and the Higher Edu-cation Act of Rhineland-Palatinate (§ 56 Hochschulgesetz – Higher Education Act). The ideal candidate has a master’s degree in computer
science, mathematics, or related fields. Experience with natural language processing is a plus. Candidates must have excellent programming and mathematical skills, expert knowledge in machine learning, be pro-ficient in oral and written English, possess
excellent communication skills, and be team- and result-oriented.

What we have to offer:
You will be part of a highly qualified international team and a stimulating work environment. We offer you comprehensive additional services, such as the pay-ment of an annual special payment, participation in the additional pension scheme in the public sector via the VBL, the possibility of purchasing a job ticket, extensive personnel development offers and flexible working hours.

The position is paid according to EG 13 TV-L and to be filled as soon as possible. The position is limited in time to one year, with an expected extension to a total of 3 years after a positive initial evaluation. The suc-cessful applicant is expected to work on their PhD thesis during that time.

JGU is diverse and welcomes qualified applications from people with varied backgrounds.

We aim to increase the number of women in the field of research and teaching and therefore encourage
female researchers to apply.

Candidates with severe disabilities and appropriate qualifications will be given priority.
Interested candidates should send their motivation letter, CV, transcripts, and the contact information of three referees in one PDF file no later than March 31, 2023, by stating the identification no: 00323-08-wiss-nk via email to Prof. Dr. Katharina Kann:

kkann@uni-mainz.de