News

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