We are one of the largest and oldest universities in Europe and one of the most important employers in our region.
Our broad range of subjects, the dynamic development of our main research areas and our central location in Cologne
make us attractive for students and researchers from around the world. We offer a wide range of career opportunities in
science, technology, and administration.

The position combines independent academic research in machine learning and artificial

intelligence with a coordination role at the newly established ELLIS Unit NRW (https://ellis-

unit-nrw.github.io/) and strategic work at the intersection of AI and Earth System Sciences

(ESS). It is embedded within Professor Bojchevski‘s research group in the Department of

Computer Science. The successful candidate will advance robust, scalable, and reprodu-

cible ML/AI methods and help translate recent AI innovations into sustainable scientific

workflows for Earth System Sciences (ESS).

YOUR TASKS

» Conduct independent research on ML/AI methods within the Key Profile Area Intelli-

gent Methods for Earth System Science (IMfESS) https://imfess.uni-koeln.de)

» Support KPA IMfESS research groups in model design, evaluation, and deployment

» Support deployment of (scalable) ML pipelines, standardize workflows and documen-

tation and contribute to shared code

» Contribute to training formats (e.g. summer schools) and interdisciplinary teaching at

the interface of AI and ESS.

» Support the coordination of the ELLIS Unit NRW and further collaborative research

initiatives

YOUR PROFILE

» Completed doctoral degree, or doctoral degree close to completion, in Compu-

ter Science, Machine Learning, Artificial Intelligence, Data Science, Computational

Sciences, or a related field.

» Strong background in ML/AI.

» Prior experience in scientific ML, foundation models, and spatio-temporal modeling is

desirable but not required.

» Prior experience in Earth System Sciences is desirable but not required

» Excellent programming skills and experience with modern ML frameworks, reproduci-

ble workflows, and collaborative software development.

» Experience with scalable ML workflows, high-performance computing, cloud infras-

tructure, or research software engineering is an advantage

» Interest in interdisciplinary collaboration at the interface of AI, Computer Science, and

Earth System Sciences.

» Strong communication, organizational, and documentation skills, and very good

written and spoken English.

WE OFFER

» Scientific independence, combining ML/AI research with strategic coordination

» A diverse working environment with equal opportunities

» Support in balancing work and family life

» Flexible working time models

» Extensive advanced training opportunities

» Occupational health management offers

The University of Cologne promotes equal opportunities and diversity. Women will be con-

sidered preferentially in accordance with the Equal Opportunities Act of North Rhine-West-

phalia (Landesgleichstellungsgesetz – LGG NRW). We also expressly welcome applications

from all suitable candidates regardless of their gender, nationality, ethnic and social origin,

religion, disability, age, sexual orientation and identity.

The position is available from 1 October 2026 on a full-time basis (39,83 hours per

week). The position is to be filled for a fixed term until 30 September 2029. If the

applicant meets the relevant wage requirements and has the appropriate personal

qualifications, the salary is based on remuneration group 13 TV-L of the pay scale for

the German public sector.

Please apply online with proof of the required qualifications (without a photo) under

https://jobportal.uni-koeln.de The reference number is Wiss2606-13. The application dead-

line is 20 July 2026.

For further inquiries, please contact Professor Aleksandar Bojchevski at

(bojchevski@cs.uni-koeln.de) and take a look at our FAQs.

Postdoctoral Researcher / ML Scientist for the KPA IMfESS (f/m/x)

Faculty of Mathematics and Natural Sciences

Department of Computer Science