Open position: post-doc in “Neural Feature and Representation Learning
for Translation and Translation Technology” at Saarland University and
DFKI (German Reserach Center for AI)
The Universität des Saarlandes (UdS, www.uni-saarland.de) and the German
Research Center for Artificial Intelligence (DFKI, www.dfki.de) are
opening a post-doctoral position (post-doc) in:
“Neural Feature and Representation Learning for Translation and
Translation Technology”.
The position is funded by the Collaborative Research Cluster (CRC)
"Information Density and Linguistic Encoding" (SFB 1102
www.sfb1102.uni-saarland.de/) Project B6 at UdS and the Deeplee project
(https://www.deeplee.de) at DFKI. The projects are complementary in
methodology and objectives and are lead by the same PIs. Successful
applicants will be employed both at UdS and DFKI, with separate contracts.
Responsibilities: fundamental research, publication of research
outcomes, ML software development, contribution to supervision and teaching.
Requirements: PhD in language technology, computer science, machine
learning (or similar); strong background/publications in MT/NLP, machine
learning, deep learning; strong problem solving and programming skills;
independent and creative thinking; strong team working and communication
skills; excellent command of written and oral English.
Command of German and/or other languages helpful, but not a requirement.
Application deadline: 28th February 2019
Start date: as soon as possible
Location: UdS and DFKI, Campus Saarland University, Saarbrücken, Germany
Duration: 2 years.
Research:
SFB B6: translated text shows characteristics that distinguish it from
comparable text originaly authored in the target language. These
characteristics are often refered to as "transationese". Machines (ML)
are good at distinguishing originally authored from translated texts. To
date research has mostly focused on traditional human
feature-engineering based supervised ML for translationese
classification. Research objectives: investigate (i) the performance of
deep learning based representation learning, (ii) whether the
features/representations learned support linguistic and/or
information-theoretic interpretations (e.g. Shannon suprisal,
information density), (iii) whether insights obtained can improve (N)MT.
DFKI Deeplee: given enought data, neural approaches often outperform
alterantive approaches in NLP. Research objectives: (i) cary out
foundational research in neural machine translation (NMT), including
(ii) NMT architectures, (iii) use of data, (iv) inclusion of (external)
knowledge sources and (iv) explainability of models.
Successful applicants will work in the Collaborative Research Cluster
"Information Density and Linguistic Encoding" in the Language Science
and Technology (LST) Department at the UdS, and the Multilingual
Technologies (MLT) Lab at DFKI. Both LST and MLT are leading centres for
language technology research and provide dynamic and stimulating
international research environments.
The UdS Campus hosts top-ranked collaborating research institutions
including the German Research Centre for Artificial Intelligence (DFKI),
the Max Planck Institute for Informatics (MPI-INF), the Max Planck
Institute for Software Systems (MPI-SWS), the Helmholtz Center for
Computer Security (CISPA), the Center for Bioinformatics (CBI) and the
Computer Science Department.
Applications should include: short cover letter, CV, list of
publications, brief summary of research interests, contact information
for two references.
Please send your electronic application (PDF) to Dr. Raphael Rubino
(raphael.rub...@dfki.de) and Prof. Josef van Genabith
(josef.van_genab...@dfki.de) referring to the position (post-doc
SFB/DFKI). The position remains open until filled.
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