The Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE) provides an interdisciplinary environment for educating the next generation of data scientists in close contact to domain-specific knowledge and research. All three domains - life & medical sciences, earth sciences, and energy systems/materials - are characterized by the experimental generation of huge heterogeneously structured data sets, which have to be evaluated in order to obtain a holistic understanding of very complex systems.

Project overview
The PhD-candidate will set-up an ensemble of runs with the model TSMP-PDAF for the African continent, which simulate water, energy and biogeochemical cycles for the subsurface and land surface including multiscale assimilation of various remote sensing products. The simulations allow the quantification of the impact of land use land cover change and human water use on the changes of the terrestrial water, energy and carbon cycles over the African continent, conditioned to remotely sensed satellite products. In order to explore the large amount of output data generated by the ensemble simulations at high spatial resolution (more than 100 TB), parallel big data analytical methods are an important tool. Besides classical uni-, bi- and multivariate statistics, and time series analysis, also methods suited to detect more complex patterns in space and time like wavelet analysis and machine learning (ML) algorithms will be used.

Further information on HDS-LEE is available at: www.hds-lee.de

We are offering from 01.07.2019 a

PhD position in simulation sciences

Your Job:

• set-up ensemble of TSMP-models over the African continent
• further develop continental-scale data assimilation system
• perform simulation runs including data assimilation analyzing the impact of land use land cover change, human water use and soil property uncertainty
• perform extensive analyses of the simulation results using big data analytics methods
• contribute to an international research team, publish in international journals and present results at international conferences

Your Profile:

• university degree (Master) in either simulation sciences, applied mathematics, geosciences, physics, civil engineering or computer science
• a high level of scholarship as indicated, for example, by bachelor and master study transcripts and two reference letters
• experience in modeling, data analysis
• knowledge on inversion or data assimilation is a clear advantage
• programming skills like Python, R, Fortran or C/C++ and experience with HPC-systems are also a clear advantage
• ability to work independently as well as collaboratively in an international, interdisciplinary team; good communication and organizational skills
• very good command of the English language: TOEFL or equivalent evidence of English-speaking skills
• excellent communication and organizational skills

Our Offer:

• HDS-LEE program with comprehensive training courses, e.g. in parallel computing, machine learning and deep learning, visualization and scientific computing
• outstanding scientific and technical infrastructure - ideal conditions for successfully completing a doctoral degree
• a highly motivated group as well as an international and interdisciplinary working environment at one of Europe's largest research establishments
• chance of participating in (international) conferences and project meetings
• continuous scientific mentoring by your scientific advisor
• doctoral degree conferred by RWTH Aachen University
• further development of your personal strengths, e.g. via a comprehensive further training programme
• pay in line with 100 % of pay group 13 of the Collective Agreement for the Public Service (TVöD-Bund)
• usually a contract for the duration of 3 years

Forschungszentrum Jülich aims to employ more women in this area and therefore particularly welcomes applications from women.

We also welcome applications from disabled persons.

We look forward to receiving your application, preferably via our online recruitment system on our career site until 30.04.2019, quoting the reference number 2019D-121.