Students (M/F/d) for Master Thesis Machine Learning Methods in Numerical Weather Prediction
vor 1 Woche
// Zentrum für Sonnenenergie
- und Wasserstoff-Forschung Baden-Württemberg (ZSW)
**Wir suchen aktuell: Students (M/F/d) for Master Thesis Machine Learning Methods in Numerical Weather Prediction in Stuttgart**:
**// Energy with a future**
The Centre for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW) is one of the leading institutes for applied research into the major issues relating to the energy transition which include photovoltaics, wind energy, battery technology, fuel cells, electrolysis, e-fuels, circular economy, policy advice, and the use of AI for process and system optimisation. We work in tandem with industry to pave the way to market for new technologies. The challenge falls to more than 300 employees and around 100 research and student assistants at the ZSW bases in Stuttgart and Ulm. The ZSW also operates a test site for wind energy and another test site for PV systems. The ZSW is a member of the Innovationsallianz Baden-Württemberg (innBW), an alliance of ten applied research institutions.
For the department Systems Analysis (SYS) at the Stuttgart site, we are seeking as soon as possible a student (M/F/d) to work on a
**Master Thesis (M/F/d) in the field of Machine Learning Methods in Numerical Weather Prediction**:
In 2022 a group of researchers affiliated to Google published GraphCast ([Lam et al., arXiv:2212.12794]), a Graph Neural Network model for global weather forecasting. This model is a data-driven surrogate for one of the most advanced global numerical weather prediction (NWP) models, the Integrated Forecasting System High Resolution Model (IFS HRES) developed and operated by the European Centre for Medium Range Weather Forecasting (ECMWF). The IFS HRES forecasts the global state of the atmosphere on a grid with 9 km horizontal resolution on 137 vertical levels in 6 hour increments.
**Your responsibilities**:
- Description of the GraphCast model architecture and data basis
- Review of alternative approaches to data-driven weather forecasting (FourCastNet, FuxiWeather, PanguWeather)
- Review of the current state of 'derivative' research building on the GraphCast results with a focus on GNN modells
- Implementation of a framework to run GraphCast predictions on architecture provided by ZSW
- Evaluation of GraphCast predictions against ICON-D2 data
- Translating the Local Area Modeling approach [Oskarsson et al., arXiv:2309.17370] to ICON-D2 domain
- Training a GraphCast-based Local Area Model on historical ICON-D2 forecasts
- Evaluating model configurations
**Your profile**:
- Extended studies in a field relevant to a subset of the domains of Deep Learning, Optimization, Meteorology
- Competence in Python programming, especially a familiarity with machine learning frameworks (e.g. pytorch, tensorflow)
- Enthusiasm for working on problems in applied research
- Expertise in the domain of Numerical Weather Prediction is a huge benefit
- so is expertise in working with Linux-based operating systems
- good proficiency in German
**Our offer to you**:
- Creative working atmosphere: We have an open corporate culture with flat hierarchies and plenty of scope for creative development
- Flexible working hours
- Good public transport connections and bicycle parking spaces with service area
- Modern institute building with first-class infrastructure and laboratory structure offer an attractive working environment
- Company health management with numerous sports and health courses and employee discounts
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