- demonstrated knowledge of one or more components of the climate system (in particular ocean/atmosphere interaction, and/or atmospheric dynamics, and/or ocean dynamics, and/or dynamics of teleconnections)
- demonstrated experience in the application, development, and implementation of advanced machine-learning techniques in climate models, or at least individual components
- demonstrated interest in understanding European summer heat extremes, ideally demonstrated through previous publications on the topic
- profound interest in interdisciplinary dialogue and research approaches
- ability to both work in a team and to conduct independent work
- strong oral and written communication skills
- excellent organizational skills
- Reliable remuneration based on wage agreements
- Continuing education opportunities
- University pensions
- Attractive location
- Flexible working hours
- Work-life balance opportunities
- Health management
- Educational leave
- 30 days of vacation per annum
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Research Associate for the Project "EXPECT" § 28 Subsection 3 HmbHG - Hamburg, Deutschland - Universität Hamburg
Beschreibung
Research Associate for the Project "EXPECT" § 28 Subsection 3 HmbHG
Institution Faculty of Mathematics, Informatics and Natural Sciences, Department of Earth System Sciences, Institute of Oceanography
Salary level
Start date , fixed until This is a fixed-term contract in accordance with Section 2 of the academic fixed-term labor contract act [Wissenschaftszeitvertragsgesetz, WissZeitVG]).
Application deadline
Scope of work full-time position suitable for part-time
Your responsibilities
Duties include academic services in the project named above. Research associates may also pursue independent research and further academic qualifications.
For the work in the research project EXPECT - Towards an Integrated Capability to Explain and Predict Regional Climate Changes () funded by the EU, we seek a candidate with an interest in seasonal-decadal climate predictions. EXPECT will identify and quantify the mechanisms by which physical processes govern regional climatic changes, including extremes, on inter-annual to multi-decadal time scales. It will do so by exploiting newly available climate simulations and Earth Observations (EOs), and by combining machine learning (ML) with physical methods. The research will target fundamental knowledge gaps related to atmospheric circulation and land-atmosphere interactions, which represent major limitations in current climate predictions and projections, and in particular in understanding changes in European summer extremes.
The applicant will work to use machine-learning techniques to improve the skill of seasonal-decadal predictions of European summer extremes. A particular focus of the successful applicant's work will be on building machine-learning based analysis and post-processing techniques for seasonal-decadal forecast information. Improving forecast skill includes running dedicated ensemble simulations and analysis with ESM based seasonal-decadal prediction systems, conditioning the predictions on physical boundary conditions.
The successful applicant with a strong background in climate modelling and geophysical fluid dynamics will work in the research groups 'Climate Modelling' and 'Climate Extremes' at Universität Hamburg.
Your profile
A university degree in a relevant field, plus doctorate.
We expect an academic degree in applied mathematics, physics, meteorology, oceanography, or computational science, plus a doctoral degree/PhD in a relevant field. We expect the following skills:
We offer
Universität Hamburg—University of Excellence is one of the strongest research educational institutions in Germany. Our work in research, teaching, educational and knowledge exchange activities is fostering the next generation of responsible global citizens ready to tackle the global challenges facing us. Our guiding principle "Innovating and Cooperating for a Sustainable Future" drives collaboration with academic and nonacademic partner institutions in the Hamburg Metropolitan Region and around the world. We would like to invite you to be part of our community to work with us in creating sustainable and digital change for a dynamic and pluralist society.
Severely disabled and disabled applicants with the same status will receive preference over equally qualified non-disabled applicants.