We are looking for a student worker to support our research in the new DFG-funded Collaborative Research Center FONDA, which investigates optimization of large-scale scientific data analysis workflows. More specifically, the project aims to support scientists in finding efficient methods for implementing and executing machine learning based data analysis workflows for monitoring forest disturbances, allowing them for rapid deployment of remediation actions.
At the research group DOS of Prof. Odej Kao, we (1) monitor data analysis workflow performance degradation (2) develop new self-adaptive algorithms for scheduling in different environments (3) research methods for efficient and stable maintenance of machine learning based data analysis workflows and (4) research computational efficiency with incremental learning.
The student will be fully involved in the collaborative research and, thus, gain comprehensive insights into scientific practices.
The student's responsibilities include:

• Preparing our infrastructure-specific execution profiles for the implementation of scientific experiments (40 %)
• supporting execution of scientific experiments (30 %)
• supporting the team in research (e.g. literature research, framework testing etc.) (30 %)

Must:

• A good understandlng of distributed systems and machine learning
• Solid programming skills preferably in Python
• Good knowledge of Linux and command line tools
• Very good knowledge of English and good knowledge of German both written and spoken

Can:

• Familiarity of Remote Sensing
• Working knowledge of machine learning frameworks like TensorFlow or PyTorch
• Experience in working with distributed porcessing systems like Spark or Nextflow
• Good communication skills

Party responsible for specialist area / point of contact for job posting: Diellza Sherifi/ Prof. Odej Kao
Period of employment: immediately for 2 years

Please submit your written application including your CV, certificate of enrollment, and where applicable, current transcript of records, to the place of employment indicated above.
In the interest of promoting equality opportunities for men and women, applications from women with suitable qualifications are particularly encouraged.