Monday, June 19, 2017

[DMANET] 2 PhD positions at LMU Munich - Algorithmic Genomics

The Canzar lab at the Gene Center of the Ludwig-Maximilians
Universität Munich is recruiting two PhD students in algorithmic
genomics, to start in fall 2017. The position provides a competitive
salary for a period of 4 years.

The successful applicant will develop and engineer efficient
algorithms for analyzing Big Data in genomics, from (single-cell)
RNA-seq to third-generation technologies like PacBio and Oxford

Supporting the lab's agenda, the successful candidate will employ
tools and techniques from combinatorial optimization and machine
learning to reveal biological clues buried under billions of short
genomic sequences. Translated into scalable and usable software, they
will equip scientists around the world with powerful tools to get the
most from the many petabytes of sequencing data produced every year.

The Canzar lab is embedded in the stimulating, interdisciplinary
environment of the Graduate School for Quantitative Biosciences Munich
(QBM), which integrates approaches ranging from (bio)informatics and
theoretical biophysics to molecular systems biology. The lab is housed
in the new Research Center for Molecular Biosystems (BioSysM) at the
life sciences campus Grosshadern/ Martinsried, with excellent access
to other parts of Munich via public transport.

We are seeking motivated candidates with an excellent Master's degree
(or equivalent) in computer science, (applied) mathematics, or
bioinformatics to join our lab. We expect the candidate to have a
solid background in algorithm design and good programming skills as
well as an interest in interdisciplinary work (biology, medicine).

To apply, send your application materials, including a motivation
letter, your CV, and names of two referees in a single pdf file to
Stefan Canzar (, who can also supply further
information about this position. For more information about the lab at
LMU, please visit our website at

Applications will be reviewed starting from July 10, 2017.

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