- Veröffentlicht: Montag, 01. Oktober 2018 07:45
Location: Heidelberg, Germany
Staff Category: Staff Member
Contract Duration: 3 years (renewable)
Grading: 4, 5 or 6; depending on qualification and experience
Closing Date: 04 November 2018
Reference Number: HD01377
A staff position as scientific programming and software engineering is available in the Statistical Genomics and Systems Genetics group at our laboratory in the Genome Biology Unit at EMBL Heidelberg in Germany.
Our research group is relocating from Cambridge to Heidelberg, where we bridge the excellence in molecular biology and biotechnology at EMBL Heidelberg with disease models and access to large biomedical datasets at the German Cancer Research Center Heidelberg.
The programmer will lead the development of Kipoi (http://kipoi.org), a software repository and API that seeks to unify recent advances in machine learning and deep neural networks for regulatory genomics. Kipoi builds on 3-way collaboration with international partners (Gagneur lab, LMU Munich, Kundaje lab, Stanford), and is increasingly used and extended by the research community. The position is funded via the recently awarded BMBF project MechML, which we are coordinating. The core aims of the post is to maintain and extend the Kipoi framework and its API, to implement new models within Kipoi and to support users of the system. We are also seeking to expand Kipoi to new fields and domains, including imaging and single-cell genomics. The latter aims are closely connected to the Human Cell Atlas, to which our group contributes as a node in the analysis working group.
You will be located in the Stegle group and collaborate with partners in the MechML projects, collaborators at EMBL, DKFZ and elsewhere. We seek to build on previous developments and expertise in the group, including in deep learning methods (see below). The position will be jointly located at EMBL Heidelberg and DKFZ.
Recent relevant publications:
Argelaguet, R., et al. (2018). Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets. Molecular Systems biology, 14, e8124.
Buettner, F., et al. (2017) f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq." Genome biology 18.1 (217): 212.
Angermueller, Christof, et al. (2017) DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning.Genome biology 1 (2017): 67.
Angermueller, Christof ,et al. (2016) Deep learning for computational biology. Molecular Systems Biology, 12, 878.
Buettner, F., et al. (2015). Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nature biotechnology, 33(2), 155.
The successful applicant will hold a doctoral degree or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering with demonstrated experience in scientific programming, ideally combined with computational and statistical methods development.
Previous experience the management of substantial software projects is expected. Expertise in deep learning, the development of methods for genomics and genetics is beneficial, as is communicating results in scientific conferences and papers.
We especially seek candidates with prior experience in the development of software systems that utilize machine learning methods, including methods based on deep learning.
Proficiency with a high-level programming language (e.g., C++, Java) and/or appropriate scripting languages, and statistical data analysis tools such as R, MATLAB or Python is required.
The ideal applicant should have demonstrated the ability to work independently and creatively. (S)he should have excellent communications skills and be able to articulate clearly the scientific and technical needs, set clear goals and work within an interdisciplinary setting, communicating with other partners within the MechML project and within the Human Cell Atlas project.
You might also have
A good foundation in, and previous usage of methods in any of the following fields is advantageous: statistics, machine learning, genetics, optimization and mathematical modelling. A background in molecular biology, or previous experience tackling biological questions is not necessary.
Why join us
Why not! Well, EMBL is an inclusive, equal opportunity employer offering attractive conditions and benefits appropriate to an international research organisation with a very collegial and family friendly working environment. The remuneration package comprises from a competitive salary, a comprehensive pension scheme, medical, educational and other social benefits, as well as financial support for relocation and installation, including your family and the availability of an excellent child care facility on campus. EMBL has a large thriving community of bioinformaticians, working in close collaboration with experimental scientists and with strong links to other local scientists and institutions.
What else do I need to know
We are Europe’s flagship research laboratory for the life sciences – an intergovernmental organisation performing scientific research in disciplines including molecular biology, physics, chemistry and computer science. We are an international, innovative and interdisciplinary laboratory with more than 1600 employees from many nations, operating across six sites, in Heidelberg (HQ), Barcelona, Hinxton near Cambridge, Hamburg, Grenoble and Rome.
Our mission is to offer vital services in training scientists, students and visitors at all levels; to develop new instruments and methods in the life sciences and actively engage in technology transfer activities, and to integrate European life science research.
Please note that appointments on fixed term contracts can be renewed, depending on circumstances at the time of the review.
Please apply online through: www.embl.org/jobs