Vacancy-ID:  5800 
Place of work:  Jena 
Starting date:  as soon as possible 
Career level:  Graduates; Experienced professionals 
Type of employment:  Full-time; Part time 
Duration of contract:  3 Jahre 
Remuneration:  Remuneration is in accordance with the Collective Agreement for the Public Sector - Federal Government (TVöD-Bund)

At the Institute of Data Science in Jena, we are working to make the data backbone for all of DLR’s areas of application (aviation, space, energy, transport, security) a reality. To this end, we conduct interdisciplinary research and method development with a focus on applications such as sustainable and circular processes, resilient supply chains, data-driven value chains and robust decision support. The methods developed in this way are put into practice in cooperation with other DLR institutes and external partners, whether as part of joint projects or through technology transfer activities.

What to expect

The department Data Analysis and Intelligence develops and applies methods that enable the analysis of complex and large datasets. To this end, the department leverages techniques from machine learning and causal inference, as well as domain-specific process knowledge.

The research group “Causal Inference” has the scientific objective to contribute to a data-driven understanding of complex dynamical systems. To this end, the group develops and applies methods and software from the fields of causal inference and statistical learning. In doing so, the group follows an application-driven approach. In addition to closely collaborating with the users of the developed methods, this means to identify requirements that arise in applications and to address these requirements by further method development. The group has a special focus on time series data, and it also works on quantum machine learning.

Your tasks

• Literature reviews with the aim to critically evaluate methods and software from the fields of statistics, machine learning and spatiotemporal modeling, and to adapt these for one’s own work
• Development of concepts for the (further) development of algorithms for spatiotemporal statistical modeling, including uncertainty estimation
• Implementation of these concepts into algorithms written in Python, and application of these algorithms to synthetic test datasets and/or real-world datasets
• Evaluation of the performance of these algorithms by systematically analyzing the applications using appropriate metrics (e.g., sensitivity, specificity, computation time)
• Documentation of the implementations, applications and performance evaluations
• Screening of the work results with regard to a potential for patentability and, where applicable, collaborative work in the process of patent applications
• Writing scientific publications based on the work results for submission to specialist journals and/or scientific talks for presentation at conferences, workshops, trade fairs, etc.

Your qualifications

• A completed university degree (Master’s / University Diploma) in Mathematics, Physics, Statistics, Computer Science, Data Science or another field of study that is relevant to the role
• Expertise in the field of spatiotemporal statistical modeling
• First experience in working on research tasks, preferably with a focus on spatiotemporal statistical modeling
• Very good programming skills in Python
• Very good written and spoken English
• Experience in writing scientific publications

 We look forward to getting to know you!

If you have any questions about this position (Vacancy-ID 5800) please contact:

Prof. Christian Thiel 
Tel.: +49 3641 30960 128