Technische Universität Berlin - Faculty IV - Institute of Telecommunication Systems / Internet Architecture and Management (INET)
Research Assistant - salary grade E13 TV-L Berliner Hochschulen - 1st qualification period (PhD candidate)
part-time employment may be possible
The Chair "Internet Architecture and Management" aims at the investigation of future communication networks and future applications offered through these networks. Our vision is that networked systems should become self-* (i.e., self-optimizing, self-repairing, self-configuring). Accordingly, we are currently particularly interested in automated and data-driven approaches to design, optimize, and verify networked systems.
Traditionally and still today, communication networks and CDNs are operated in a rather static and conservative manner. However, it is known that in principle a more dynamic network operation can greatly improve the efficiency of networks: empirical studies show that communication traffic and resource popularity feature much structure, i.e., are bursty and skewed. More dynamic network operations are also useful to support adaptive policy changes, service relocations, or emerging “self-driving networks". This project is motivated by the desire to leverage the benefits of more flexible network operations but without sacrificing the reliability guarantees of more conservative operations.
Lay the theoretical and algorithmic foundations of more adaptive communication networks and CDNs, using tools from machine learning (ML) and artificial intelligence (AI), as well as optimization techniques in general.
Develop methods for resource popularity prediction, which can in turn be used to optimize CDN prefetching algorithms.
Evaluate our theoretical concepts in practice, using access log data and real traces from deployed networked systems, partly also in collaboration with industry.
Involvement in teaching and the organization of teaching; supporting the head of the department in supervising bachelor and master thesis.
Dissemination of research results: describe the results in scientific publications and posters; presentation of research results on international conferences and workshops or presentation to industry partners.
We are looking for a highly motivated and bright PhD student with a strong expertise and interest in applying ML/AI and optimization methodologies to improve the efficiency of communication networks in general, and content distribution networks (CDNs) in particular.
Successfully completed university degree (Master, Diplom or equivalent) in computer science, computer engineering or a related field.
Competence in communication networks including content distribution networks is required.
Competence in algorithms, esp. network algorithms, distributed algorithms and online algorithms is required.
Competence in optimization (e.g. linear programming), machine learning and AI is required.
Excellent command of written and spoken English is required. High ability to express yourself both, orally and in writing is required.
Basic experience in research methods and academic writing are a plus.
Teaching experience and didactic competence are a plus.
Ability to work in a team of international researchers and good communications skills are desired.
The ability to teach, both in German and in English, is required.
How to apply:
Please send your application with the reference number and the usual documents as your Curriculum Vitae, your diploma including the performance record and other documents by email to Prof. Dr. Stefan Schmid at firstname.lastname@example.org.
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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.
Technische Universität Berlin - Der Präsident -, Fakultät IV, Institut für Telekommunikationssysteme, FG Internet Architecture and Management (INET), Prof. Dr. Stefan Schmid, Sekr. EN 18, Einsteinufer 17, 10587 Berlin