Job offer
Role details
Job location
Tech stack
Job description
- Independent research on methods of interpretable/explainable machine learning with the goal of earning a Ph.D.
- Design, implementation, and evaluation of XAI approaches, as well as publication of the results at international conferences and in academic journals
- Teaching support (e.g., exercises, seminars, or assistance with lectures in the field of machine learning/explainable AI) in accordance with the respective teaching load
- Supervision of thesis projects (bachelor's/master's) and assistance with student supervision, * Secure and transparent remuneration in accordance with the collective agreement of the federal states as well as additional benefits such as an annual special payment and company pension scheme
- Flexible working hours, 30 days' vacation and fair rules for balancing work and family life
- Homeoffice - proportionate opportunity for mobile working
- Together, we are advancing science and society on this lively and dynamic campus
- Strong support for your own academic career
- A wide range of offers for the development of your talents and perspectives
- Collaboration in a dynamic international team with diverse, varied and responsible tasks
Selection process
Are you ready for a new challenge and interested in this varied and responsible position? Then submit your complete application by 8/18/2026, via the "Apply now" button. Please submit the following documents:
- Cover letter and tabular CV with the key details
- Key facts (education/studies, internships, project and work experience)
- Certificates/transcripts (Bachelor's, Master's, any additional certificates), with German recognition/equivalency where applicable
- A writing sample: your Master's thesis and, if applicable, further publications, totaling no more than 40 pages
- Link to GitHub profile or portfolio of relevant projects (if available)
- A bullet-point list of your programming skills (languages, frameworks, tools) and your in-depth knowledge in machine learning/explainable AI
You may only be employed in the civil service of the state of Rhineland-Palatinate if you can guarantee that you will uphold the free democratic basic order as defined in the Basic Law and the Constitution of Rhineland-Palatinate at all times. Additional comments
Requirements
To strengthen the XplaiNLP group (https://xplainlp.github.io/ ) in the Business Informatics program of Faculty 03 - Law and Economics, we are seeking a motivated and dedicated academic staff member to start as soon as possible., Master Degree or equivalent, * excellent completed scientific university (Master's or equivalent), preferably in Computer Science or a closely related field, ideally with a focus on Machine Learning, Artificial Intelligence, or Explainable AI (XAI)
- In-depth and sufficiently broad knowledge and skills in machine learning as well as explainability methods (e.g., interpretable machine learning, post-hoc explainability, concept-based explanations, causal inference)
- Very good programming skills (especially Python) and experience with common ML frameworks (e.g., PyTorch, TensorFlow)
- Experience in university teaching appropriate to career stage, preferably in machine learning/explainable AI, is an advantage
- Very good English skills (at least C1 level)
- Sufficient German skills to teach in German (at least B1 level is desirable)
- A high degree of initiative, personal responsibility, communication, organizational, and teamwork skills, along with enthusiasm for scholarly exchange and knowledge dissemination
In addition to the general requirements according to public services law, applicants must meet the recruitment requirements stipulated in § 57 of the Hochschulgesetz of Rhineland-Palatinate.
Languages ENGLISH
Benefits & conditions
The position is paid according to EG 13 TV-L and is to be filled immediately. The position is limited to a period of 4 years. An extension of the employment period may be possible.
The position serves the purpose of academic qualification (doctorate).
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.
People with severe disabilities and people with disabilities who are treated equally in accordance with Section 2 (3) SGB IX will be given preferential consideration if they are suitable.