Below you can find information about open positions at our Chair. All open positions are in the Würzburg lab. We do not hire new personell nor do we accept applications for (summer) internships or stays in our Zurich lab.
Starting in the second half of 2024, we will have an opening for a postdoc position (100 % employment level, TV-L E13 or E14 grade depending on qualification) to work on machine learning for complex networks, with possible applications in software engineering, biology, and ecology.
Successful applicants most hold a PhD degree in a field that is clearly related to AI, data or network science, e.g. computer science, mathematics, engineering, natural sciences. Suitable candidates have further demonstrated their ability to publish research in leading venues of machine learning, network science, and/or data science (either in high-quality international conferences or interdisciplinary journals). Excellent writing and communication skills in English are a must. German language skills and experience in teaching or student supervision is considered a plus. We welcome applications from all qualified candidates, irrespective of gender, disability, marital or parental status, nationality or ethnicity.
Interested applicants shall send the usual application documents (motivation letter, curriculum vitae, publication list, certificates, name of potential references) to Prof. Dr. Ingo Scholtes while citing the reference code ML4Nets_PD_24 in the subject of your E-Mail. All documents must be submitted in PDF format. Complete applications will be reviewed on a rolling basis until the position is filled.
Starting in the first quarter of 2024, we will have an open position for a PhD student (100 % Employment level, TV-L E13 grade) working on a third-party funded project at the intersection of software engineering, AI-driven project management, and graph learning.
Successful applicants must hold an excellent MSc degree (or equivalent, e.g. Diploma) in an AI-related discipline, e.g. computer science, mathematics, engineering, computational social science, or natural sciences. Applications must also have proven software development skills as well as experience with web-based technologies and data visualization. Excellent language skills in English or German are a precondition for this position. A MSc thesis topic that shows a clear relation to machine learning, data science, and/or software engineering is considered a plus. We welcome applications from all qualified candidates, irrespective of gender, disability, marital or parental status, nationality or ethnicity.
Interested applicants shall send the usual documents (motivation letter, curriculum vitae, MSc thesis, grade transcript and certificates, name of possible references) to Prof. Dr. Ingo Scholtes, while citing the reference code ML4Nets_SC_24 in the subject of the E-Mail. All documents shall be submitted in PDF format.
We currently have multiple open positions for student assistants to support us in the development of the python network analysis package pathpy, e.g. by implementing basic network analysis algorithms, development of unit tests, preparation of tutorials, or completing documentation.
The ideal candidates for this position have programming experience in python, are proficient with jupyter notebooks, and have worked with standard data science and numerical computing packages like sklearn, pandas, numpy, scipy, or numba. Work hours are negotiable. The completion of one of our MSc-level courses on Statistical Network Analysis or Machine Learning for Complex Networks is considered a plus. We welcome applications from all qualified candidates, irrespective of gender, disability, marital or parental status, nationality or ethnicity. However, the positions are restricted to students studying at University of Würzburg.
Interested students shall send a brief motivation statement, a curriculum vitae, and a grade transcript (all in PDF format) to Prof. Dr. Ingo Scholtes via E-Mail.
As a general rule, we do not offer research internships. Exception are made in special cases that are justified, e.g. by an existing collaboration or a specific research topic that promises synergies between the visiting student and members of our team. If you believe that you fall in this exceptional category, you are welcome to contact us. Please send the usual documents (motivation explaining why you want to specifically work with us, grade transcript, name of a potential reference) to Prof. Dr. Ingo Scholtes, while citing the reference code ML4Nets_Intern_23 in the subject of your E-Mail. Documents should be submitted in PDF format.
We kindly ask you for your understanding that - due to the large volume of requests that we receive - we do not have the capacity to answer requests for internships or guest stays unless they fulfill the criteria mentioned above.