The focus of the DLR Institute for Data Science in Jena is to find solutions for the major challenges of the digitalisation age. The research focuses on the areas of data extraction and mobilisation, data management and preparation, and data analysis and intelligence.
The "Causal Inference" group at German Aerospace Center’s Institute of Data Science develops theoretical foundations, algorithms, and accessible software tools for causal inference and machine learning and closely works with domain experts. Causal inference is a challenging and promising research field in artificial intelligence and its application to a broad range of domains will have a high impact both to advance science and to address topics of critical importance for the society. The core methodological topics include causal inference and causal discovery for spatio-temporal dynamical systems, machine learning, deep learning, and nonlinear time series analysis. But the methods are flexible and open for your ideas!
The position is part of the DLR project CausalAnomalies together with several DLR partners. The goal is the development of causal inference methods to better understand the causes of anomalies in various application cases ranging from Earth observation and aerospace to railroad and IT systems.
Your tasks include:
To support your international research experience, the group has a generous travel budget for conferences and extended research stays. Currently, we have collaborators at Imperial College London, Valencia, Copenhagen, Oxford, Carnegie.
Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Our unique infrastructure offers you a working environment in which you have unparalleled scope to develop your creative ideas and accomplish your professional objectives. Our human resources policy places great value on a healthy family and work-life-balance as well as equal opportunities for persons of all genders (f/m/x).Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.
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