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Are you interested in developing new machine learning training methods for large to huge data sets and have strong programming skills ideally on GPUs, then apply for our just opened PhD position!

A PhD position is available in the team of Prof. Peter Zaspel at University of Wuppertal, Germany. The position is focused on the development of novel machine learning training algorithms on GPUs in context of molecular simulations for materials design under the project “Multi-fidelity methods for fast large-scale mixed-precision molecular machine learning on GPUs”. The respective research will involve the further development of large-scale machine learning models and hardware-aware algorithms in an interdisciplinary application.

The team of Prof. Peter Zaspel is located at Bergische Universität Wuppertal. The international team focuses on machine learning, uncertainty quantification and high performance computing in context of applications from the natural sciences, engineering and beyond. It is embedded in the research group on Scientific Computing and High Performance Computing. For more details, see https://www.peter-zaspel.de/ and https://hpc.uni-wuppertal.de.

A successful applicant is expected to have a Master’s degree (or equivalent) in computer science, mathematics, physics, data science or similar discipline, strong analytical skills in context of machine learning and/or (numerical) mathematics, excellent proficiency in a programming language (preferable Python or C/C++) and interest in developing novel, hardware-aware training methods on GPUs in context of kernel-based / Gaussian Process machine learning for simulation applications. Experience in hardware-aware programming on GPUs or parallelization of algorithms is an advantage. A good command of English is essential, both as the local working language and because of our international collaborations. We look for a competent personality with initiative and commitment, who has the ability to work independently and who enjoys teaching (support).

We offer a 3 year PhD position. The salary will be paid in accordance with the Collective Agreement for the Public Service of the Federation (Tarifvertrag des öffentlichen Dienstes, TVöD Bund), with salary level 13 (75%). The position has teaching (support) duties. The place of employment will be Wuppertal, Germany.

The position is available immediately and applications will be considered until January 22, 2024. Applicants have to use the online portal of the University of Wuppertal https://stellenausschreibungen.uni-wuppertal.de, where the job offer is available under the ID 23417. There, you need to submit a letter of motivation, a CV, copies of transcripts and optionally a copy of your MSc thesis (all as one PDF). International applicants should switch to the English version of the web page via the flag symbol on the top of the page. Then, applicants will find the row with the above given ID and click on “Jetzt bewerben” on the right-hand side of that row. The remaining process is available in English. If you have questions on the position or with respect to the application process please contact Prof. Peter Zaspel via zaspel(at)uni-wuppertal.de.

Project Goals

To understand future climate change, it is critical to fully understand the past and present climate system. Information about this is encrypted in paleoclimate archives such as ice- cores or bore-hole temperatures reflecting the temperature of glaciers such as Antarctica. However, it is difficult to de-convolve and combine this knowledge as proxy records are often time-uncertain, are noisy, sparse and record the climate in very different ways.

Recently, there have been advances in understanding the ice-core recording process. Based on this, proxy system models enabling the production of “digital” cores from climate model simulations, i.e. numerical forward models, were developed. Further, bore-hole temperatures and isotope data deliver complementary information. Still, the challenge to optimally invert the process from the climate to the ice-core record and to reconstruct the climate state from such sparse, noisy and diverse data is largely unresolved.

This PhD project aims to improve on the borehole inversion methods as well as on the climate field reconstruction technique by means of modern techniques in numerical modeling and Bayesian inference to optimally combine the various information sources.  In collaboration with data science and statistics experts and experts from paleo-climate research, this project aims to develop and test a new reconstruction technique that could provide a better quantitative access of paleo-climate data and insight into the past climate evolution.

Tasks

You will

• Set up, i.e. discretize and efficiently implement forward models, given by advection diffusion equations, for glacier bore-hole temperature profiles.
• Develop and test inversion methods for the relationship of water isotopes and temperature using Bayesian inference  
• Use advanced Bayesian hierarchical modeling techniques to combine the information from water-isotopes and borehole temperatures to reconstruct the local temperature evolution. Ideally, this will be extended to spatio-temporal field reconstructions making use of the spatial physical covariance structure from reanalysis data.
  You will test this model using surrogate data from simulated (‘digital’) ice-cores –   Based on simulated cores and the developed framework, you will optimize the sampling strategy and show the feasibility and limitations of combined isotope and borehole thermometry to reconstruct the temperature evolution of Antarctica.

Requirements

• Strong analytical, mathematical and statistical skills
• Proficiency in a programming language (preferably Python or C/C++)
• A degree (Master, Diploma) in mathematics, computer science, physics, climate sciences, or a related field
• Excellent English language skills, both written and spoken
• Experience in numerical analysis and Bayesian methods is a benefit
  Previous experience with ice-cores or (paleo)climate research is an advantage.

Further information

For further information please contact Thomas Laepple (tlaepple at awi dot de) or Peter Zaspel (p dot zaspel at jacobs-university.de). The place of employment will be the Jacobs University Bremen

This position is limited to 3 years. The salary will be paid in accordance with the German “Collective Agreement for the Public Service of the Federation” (Tarifvertrag des öffentlichen Dienstes, TVöD Bund), up to salary level 13 (100%).

All doctoral candidates will be members of AWI’s postgraduate program POLMAR and thus benefit from a comprehensive training program and extensive support measures.

Background

This cutting-edge project at the interface of data-science, statistics and geochronology aims to develop and apply a methodological framework needed to fuse chronologic information from different Earth components into one consistent picture. Different Bayesian statistical models will be combined to synthesize absolute and relative age-information across published timeseries in a flexible and extendable way. This will not only result in a new approach to investigate complex systems in a data-driven way, but also in a consistently dated network of past environmental changes.

This project is part of the Helmholtz School for Marine Data Science (MarDATA) which aims to define and educate a new type of “marine data scientist” by introducing and embedding researchers from computer sciences and mathematics into ocean sciences, covering a broad range from supercomputing and modelling, (bio)informatics, robotics, to statistics and big data methodologies.

Tasks

• Develop a Bayesian approach to synthesize stratigraphic information from different paleoclimate records
• Implement and couple forward models of sediment and tracer deposition from the literature
• Apply the coupled model to existing environmental records
• Present at international conferences
• Publish in peer-reviewed scientific journals

Requirements

• a degree (Master’s, Diploma) in mathematics, computer science, physics, climate sciences, or a related field
• Strong analytical, mathematical and statistical skills
• proficiency in a programming language (preferably Python, C/C++ or R)
• excellent English language skills, both written and spoken
• experience in Bayesian statistics and/or machine learning techniques is a benefit
• previous experience with (paleo)climate research is an advantage

Further information

For further information please contact Dr. Florian Adolphi, Tel: +49(471)4831-1008, Florian dot Adolphi at awi dot de or Prof. Dr. Peter Zaspel, Tel: +49(421)200-3051, p dot zaspel at jacobs-university dot de .

This position is limited to 3 years. The salary will be paid in accordance with the Collective Agreement for the Public Service of the Federation (Tarifvertrag des öffentlichen Dienstes, TVöD Bund), up to salary level 13 (100%).

The place of employment will be Jacobs University, Bremen, with the option of short-term/interims stays at Alfred Wegener Institute, Bremerhaven.

The candidate will participate in the Helmholtz School for Marine Data Science MarDATA. All doctoral candidates will be members of AWI’s postgraduate program POLMAR or another graduate school and thus benefit from a comprehensive training program and extensive support measures.

A PhD position in Machine Learning is available in the group of Prof. Peter Zaspel at Jacobs University Bremen, Germany. The position is focused on the development of novel machine learning algorithms in context of molecular simulations for materials design. It is specifically embedded into the Priority Program SPP 2363 “Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning” funded by the German Research Foundation (DFG). The respective research will involve the further development of machine learning models and algorithms in an interdisciplinary application.

The group of Prof. Peter Zaspel is located at Jacobs University Bremen, a private, state-accredited, English-language research university. The research group focuses on machine learning, uncertainty quantification and high performance computing in context of applications from the natural sciences, engineering and beyond. For more details, see https://www.peter-zaspel.de/

This project is in close collaboration with the group of Prof. Ulrich Kleinekathöfer from the Physics Department at Jacobs University Bremen, whose group will contribute the materials design application (http://ukleinekat.user.jacobs-university.de/).

A successful applicant is expected to have a Master’s degree in computer science, physics, mathematics, data science or similar discipline, strong analytical skills in context of machine learning and/or (computational) physics / (numerical) mathematics, excellent proficiency in a programming language (preferable Python or C/C++) and interest in developing novel machine learning methodologies for simulation applications. Experience in (numerical) simulation is an advantage. A good command of English is essential, both as the local working language and because of our international collaborations.

We offer a 3 year PhD position. It is funded under the DFG grant “Multi-fidelity, Active Learning Strategies for Exciton Transfer Among Adsorbed Molecules”. The salary will be paid in accordance with the Collective Agreement for the Public Service of the Federation (Tarifvertrag des öffentlichen Dienstes, TVöD Bund), with salary level 13 (100%). The place of employment will be Bremen, Germany.

The position is available immediately and applications will be considered until the position is filled. Applicants should submit a letter of motivation, a CV, copies of transcripts and optionally a copy of their MSc thesis together with the names of two referees by e-mail to Prof. Peter Zaspel at p.zaspel(at)jacobs-university.de

A PhD position in Machine Learning or Applied Mathematics is available in the group of Prof. Peter Zaspel at Jacobs University Bremen, Germany. The position is focused on the development of novel machine learning techniques in context of a large- to extreme-scale biochemistry simulation for photosynthesis (https://doi.org/10.1016/j.cell.2019.10.021). The respective research will involve the further development of kernel-based machine learning models (Kernel Ridge Regression, Gaussian Process Regression, etc.) towards multi-fidelity models and large-scale computations in an interdisciplinary application.

The group of Prof. Peter Zaspel is located at Jacobs University Bremen, a private, state-accredited, English-language research university. The research group focuses on machine learning, uncertainty quantification and high performance computing in context of applications from the natural sciences, engineering and beyond. For more details, see https://www.peter-zaspel.de/

This project is in close collaboration with the group of Prof. Ulrich Kleinekathöfer from the Physics Department at Jacobs University Bremen, whose group will contribute the biophysics simulation application (http://ukleinekat.user.jacobs-university.de/).

A successful applicant is expected to have a Master’s degree in computer science, (applied) mathematics or similar discipline, strong analytical skills in context of machine learning and/or (numerical) mathematics, excellent proficiency in a programming language (preferable Python or C/C++) and interest in large-scale machine learning and computing applications. Experience in (numerical) simulation is an advantage. A good command of English is essential, both as the local working language and because of our international collaborations.

We offer a PhD position that is limited to 3 years. It is funded under the DFG grant “Excitation Energy Transfer in a Photosynthetic System with more than 100 Million Atoms”. The salary will be paid in accordance with the Collective Agreement for the Public Service of the Federation (Tarifvertrag des öffentlichen Dienstes, TVöD Bund), with salary level 13 (100%). The place of employment will be Bremen, Germany.

The position is available immediately and applications will be considered until the position is filled. Applicants should submit a CV, a brief statement of research interests, a copy of their MSc thesis, and the names of two referees by e-mail to Prof. Peter Zaspel at p.zaspel(at)jacobs-university.de