Research Assistant - salary grade E13 TV-L Berliner Hochschulen

Operation of complex IT infrastructures is a key for providing always-on services for data-driven and Al applications in real world settings. To respond to those needs, recently, the research community has dedicated a large effort into the field of Artificial Intelligence for IT operations AlOps. AlOps refers to multi-layered technology platforms that automate and enhance IT operations by using data analytics and machine leaming to analyze big data collected from various IT tools and devices; to automatically spot and react to issues in real-time. The observational data includes logs, metrics from monitoring tools, and traces from applied system software.
The work aims to do research and develop AlOps methods based on artificial intelligence tools and machine learning approaches against the heterogeneous observational data. The desired outcome is continuous insights that can yield continuous improvements with the implementation of automation. We focus on the following topics: data generation, data representation, anomaly detection from metric and time series data, alert correlation, root cause analysis, cloud topology detection, visualization, explanation, and data-driven decision support.
All these lead to the design of a prototype solution tested against the existing open-source system. Further experimentation and evaluation are done against test scenarios from experimental and production data.
Possibility of preparing a PhD thesis.

• Successfully completed university degree (Master, Diplom or equivalent) in Computer Science, with specialization in IT operations
• Experience with machine learning, in particular anomaly detection, root cause analysis
• Experience in managing and deploying distributed operation systems (e.g. OpenStack)
• Experience with fault tolerance techniques
• Knowledge in building and operation of containers (e.g. Singularity, Docker)
• Experience in writing and publication of scientific papers
• Experience in implementation of machine learning techniques
• Experience with TensorFlow and PyTorch
• Good knowledge of German and/or English is required; willingness to acquire the respective missing language skills

Desirable:

• Interest in system development and operation of large-scale software architecture, as weil as enthusiasm to establish recent research results in practice
• Familier with working with methods and methodologies from anomaly detection
• Experience and interest in the topics of self-supervised, unsupervised and semi-supervised machine learning, anomaly detection, causal inference and fault tolerance
• Experience developing accessible technologies
• Experience and interest in project management and agile development methodologies are preferred

Please send your written application with the reference number and the usual documents (CV, list of grades, language certificates) to Technische Universität Berlin, Prof. Odej Kao: odej.kao@tu-berlin.de.

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To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities. Applications from people of all nationalities and with a migration background are very welcome.

Tech­ni­sche Uni­ver­si­tät Ber­lin - Die Prä­si­den­tin - Insti­tut für Tele­kom­mu­ni­ka­ti­ons­sys­teme, FG Dis­tri­bu­ted and Ope­ra­ting Sys­tems, Prof. Dr. Odej Kao, Sekr. TEL 12-5, Ernst-Reu­ter-Platz 7, 10587 Ber­lin