Helmholtz Information & Data Science School:

HDS-LEE

Advance your own research with data science, learn from leading data scientists and exchange ideas across disciplines - this is possible at the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE) in the North Rhine-Westphalian ABCD triangle (Aachen-Bonn-Cologne-Düsseldorf).

Research with High Performance Computing

The international graduate school HDS-LEE provides an interdisciplinary environment for training the next generation of data scientists in close contact with domain-specific knowledge and research. It is part of the newly established JARA Center for Simulation and Data Sciences, the German competence center for computing and data infrastructures, user support, and methodological and disciplinary research in simulation, data analysis, and high-performance computing technologies. JARA is a unique cooperation between Helmholtz Research Center Jülich and RWTH Aachen University with strong international visibility.

HDS-LEE makes it possible to exchange ideas and information beyond our respective research fields. This gives us an insight into the other faculties and can also inspire a better understanding of the problems in our own areas.

Christian Gerloff

is researching the signals of the human brain at HDS-LEE - with data science. Read more about his project.

Mission

The HDS-LEE structured doctoral program aims at excellent graduates of mathematics, computer science, natural sciences and engineering from all over the world. The doctoral researchers at HDS-LEE want to advance the development of Data Science methods as well as use state-of-the-art technologies of artificial intelligence and machine learning to solve challenging scientific problems.

In the program, doctoral researchers are trained in all essential areas of Information and Data Sciences as well as in communication and other key qualifications. The training components of the program are strengthened by individually tailored training measures at the Jülich Supercomputing Center (JSC).

Research Areas

  • Life Sciences
  • Earth Science
  • Energy Systems/Material Sciences

Selection of current doctoral projects

Curriculum

  • Supervision by an interdisciplinary Thesis Advisory CommitteeMandatory lecture “Data Science Methods and Applications”
  • Courses for scientific education, including training days at the Jülich Supercomputing Center (JSC) on topics such as parallel computing, machine learning and visualization
  • Transferable skill courses: Scientific Writing, Academic Presentation, Good Scientific Practice and Doing ScienceAnnual retreat
  • Participation in (international) conferences
  • Personal competence training and comprehensive support measures for networking and career development
Workflow. Grafik: HDS-LEE
The HDS-LEE offers talented data scientists an interdisciplinary research environment in close contact with domain-specific knowledge and research. (Photo: HDS-LEE)

Funding and Duration of the Program

The program extends over 3 years and offers full funding. The remuneration during the term corresponds to the tariff level E13 of the TVöD/TV-L.  

Application and Further Information

A total of 24 doctoral positions are available, which are directly financed by HDS-LEE. In addition, interested Data Science PhD students can join the program as associated PhD students, preferably from the HDS-LEE locations Aachen, Cologne, Düsseldorf and Jülich. The program starts every three years, the next call phase starts in 2021. The program language is English.

Applicants must have adequate knowledge of computer science in general, as HDS-LEE does not offer training in computer science basics such as programming. English language skills are also required.

Are you interested in advancing your scientific research with Data Science methods? Then apply at the HDS-LEE

"At HDS-LEE, I benefit most from the training offered: the soft skills courses and, of course, the networking opportunities. The exchange with other people who have similar problems or challenges in their research is very valuable."

Mario Rüttgers, associated doctoral researcher at HDS-LEE

Contact

Dr. Ramona Kloß

Our Doctoral Researchers

Lisa Beumer

Lisa Beumer
Doctoral Researcher HDS-LEE

Leonardo Boledi

Leonardo Boledi
Doctoral Researcher HDS-LEE

Eike Cramer

Eike Cramer
Doctoral Researcher HDS-LEE

Danimir Doncevic

Danimir Doncevic
Doctoral Researcher HDS-LEE

Christian Gerloff

Christian Gerloff
Doctoral Researcher HDS-LEE

Sonja Germscheid

Sonja Germscheid
Doctoral Researcher HDS-LEE

Jorge Guzmàn

Jorge Guzmàn
Doctoral Researcher HDS-LEE

Jazib Hassan

Jazib Hassan
Doctoral Researcher HDS-LEE

Laura Helleckes

Laura Helleckes
Doctoral Researcher HDS-LEE

Robin Hilgers

Robin Hilgers
Doctoral Researcher HDS-LEE

Johann Fredrik Jadebeck

Johann Fredrik Jadebeck
Doctoral Researcher HDS-LEE

Contact

Johann Fredrik Jadebeck
Johann Fredrik Jadebeck
Project title: "Uncertainty Quantification in Metabolic Network Modelling"
Christiano Köhler

Christiano Köhler
Doctoral Researcher HDS-LEE

Johannes Kruse

Johannes Kruse
Doctoral Researcher HDS-LEE

Stephan Malzacher

Stephan Malzacher
Doctoral Researcher HDS-LEE

Contact

Stephan Malzacher
Stephan Malzacher
Project title: "Standardised data acquisition in biocatalysis according to the FAIR principles"

Supervisor

Prof. Dr. Wolfgang Wiechert

Prof. Dr. Julia Kowalski

Prof. Dr. Dörte Rother

Charlotte Neubacher

Charlotte Neubacher
Doctoral Researcher HDS-LEE

Contact

Charlotte Neubacher
Charlotte Neubacher
Project title: "Exploration of Street Canyon Observations for inner-city Air Quality Forecast and Emission Optimization"

Supervisor

Prof. Dr. Astrid Kiendler-Scharr

Prof. Dr. Axel Klawonn

Dr. Anne Caroline Lange

Bamidele Oloruntoba

Bamidele Oloruntoba
Doctoral Researcher HDS-LEE

Melven Röhrig-Zöllner

Melven Röhrig-Zöllner
Doctoral Researcher HDS-LEE

Contact

Melven Röhrig-Zöllner
Melven Röhrig-Zöllner
Project title: "Performance of Dense and Sparse Tensor Operations in Applications"

Supervisor

Dr.-Ing. Achim Basermann

Prof. Dr. Axel Klawonn

Dr. Jonas Thies

Mario Rüttgers

Mario Rüttgers
Doctoral Researcher HDS-LEE

Anna Simson

Anna Simson
Doctoral Researcher HDS-LEE

Felix Terhag

Felix Terhag
Doctoral Researcher HDS-LEE

Daniel Wolff

Daniel Wolff
Doctoral Researcher HDS-LEE

Alper Yegenoglu

Alper Yegenoglu
Doctoral Researcher HDS-LEE

Hu Zhao

Hu Zhao
Doctoral Researcher HDS-LEE

Emile de Bruyn

Emile de Bruyn
Doctoral Researcher HDS-LEE

Contact

Emile de Bruyn
Emile de Bruyn
Project title: "interaction of an intrinsically disordered protein surface in implicit or continuum solvent"
Dwaipayan Chatterjee

Dwaipayan Chatterjee
Doctoral Researcher HDS-LEE

Ann-Kathrin Edrich

Ann-Kathrin Edrich
Doctoral Researcher HDS-LEE

Contact

Ann-Kathrin Edrich
Ann-Kathrin Edrich
Project title: "Physics-informed machine learning for geohazard and climate change response engineering"

Supervisor

Prof. Kowalski

Viktor Grimm

Viktor Grimm
Doctoral Researcher HDS-LEE

Ankit Patnala

Ankit Patnala
Doctoral Researcher HDS-LEE

Contact

Ankit Patnala
Ankit Patnala
Project title: "Application on Unsupervised Learning on Air Quality Data with focus on Biogenic Emissions"
Alessio Quercia

Alessio Quercia
Doctoral Researcher HDS-LEE

Karina Ruzaeva

Karina Ruzaeva
Doctoral Researcher HDS-LEE

Moein Samadi

Moein Samadi
Doctoral Researcher HDS-LEE

Contact

Moein Samadi
Moein Samadi
Project title: "for solving the extrapolation problem of hybrid models on binary structures"
Giuliano Santarpia

Giuliano Santarpia
Doctoral Researcher HDS-LEE

Alaukik Saxena

Alaukik Saxena
Doctoral Researcher HDS-LEE

Contact

Alaukik Saxena
Alaukik Saxena
Project title: "Discovery of aluminium alloys that can be made from scraps"
Timo Stomberg

Timo Stomberg
Doctoral Researcher HDS-LEE

Karel van der Weg

Karel van der Weg
Doctoral Researcher HDS-LEE

Sophia Wiechert

Sophia Wiechert
Doctoral Researcher HDS-LEE

Kaveh Patakchi Yousefi

Kaveh Patakchi Yousefi
Doctoral Researcher HDS-LEE

Contact

Kaveh Patakchi Yousefi
Kaveh Patakchi Yousefi
Project title: "Application of Deep Learning of Observation-Model Mismatches in a Data Assimilation Context as part of the project AI Strategy for Earth System Data"
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