I have an open PhD position in Network Algorithms at the University of
Vienna, see below and at:
<https://univis.univie.ac.at/ausschreibungstellensuche/flow/bew_ausschreibun
g-flow?_flowExecutionKey=_cCBA71C1C-08F1-FAE4-37B2-3EA02D5010E9_kCBA3511D-1D
D2-5A8C-1697-4C651E9695D9&tid=67492.28>
https://univis.univie.ac.at/ausschreibungstellensuche/flow/bew_ausschreibung
-flow?_flowExecutionKey=_cCBA71C1C-08F1-FAE4-37B2-3EA02D5010E9_kCBA3511D-1DD
2-5A8C-1697-4C651E9695D9&tid=67492.28
for more details.
The specific topic will depend on the skills and interests of the student,
and could include e.g., network design, oblivious routing, graph embedding,
routing table compression, clustering or self-stabilizing algorithms.
Similarly, the methodology will depend on the student and can span from
(Integer) Linear Programming over approximation algorithms to learning and
online algorithms as well as computer-generated algorithms.
Please forward to interested students and do not hesitate to contact me for
more information!
Many thanks and kind regards,
Stefan
PS: For an overview of our research, please have a look at
<https://net.t-labs.tu-berlin.de/~stefan/>
https://net.t-labs.tu-berlin.de/~stefan/ (currently being moved to
<http://ct.cs.univie.ac.at/research/publications/>
http://ct.cs.univie.ac.at/research/publications/ )
Synopsis:
In contrast to traditional communication networks which rely on proprietary
algorithms and "blackbox" hardware, modern networks support numerous
algorithmic optimizations: in terms of routing, traffic engineering, medium
access, queuing, etc. become "programmable" and "software-defined".
Companies like Google exploit these optimization opportunities, e.g., to
efficiently schedule big data transfers across their wide-area network. Many
companies also exploit these optimization opportunities to allocate flows in
a datacenter network more efficiently and, e.g., reduce latency or job
completion time. As datacenter networks are typically also highly
virtualized, additional optimization opportunities arise: for example it is
possible to flexibly place or even migrate communication endpoints (virtual
machines or containers), to reduce communication costs (keywords: "resource
allocation" and "virtual network embedding"). Recently, it has even become
possible to change the physical network topology over time, enabling
"demand-aware networks" whose topology is optimized toward the traffic
pattern or workload they serve, and introducing novel "network design"
problems (keyword: self-adjusting networks).
Yet, the algorithmic problems underlying modern communication networks,
while practically very relevant, are not well understood today. We hence are
looking for a PhD student ("pre-doc"/"Doktorand(in)") with a strong
background and interest in algorithms (in particular, graph algorithms,
approximation algorithms, randomized algorithms, distributed algorithms,
dynamic programming, linear programming etc.). The goal of our research is
to lay the algorithmic foundations of modern communication networks, e.g.,
in areas such as routing, scheduling, clustering, and network design. It is
expected that the PhD student contributes to the modelling, algorithm
design, optimization, and formal analysis (e.g., proofs of correctness and
performance) for such networks.
--
Stefan Schmid
Professor, Computer Science
University of Vienna, Austria
<http://cs.univie.ac.at/ct/news/> http://cs.univie.ac.at/ct/news/
https://net.t-labs.tu-berlin.de/~stefan/
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