Two Snsf Phd Positions On Machine Learning for

vor 2 Wochen

Lausanne, Schweiz Université de Lausanne Vollzeit

**Introduction**:
UNIL is a leading international teaching and research institution, with over 5,000 employees and 17,000 students split between its Dorigny campus, CHUV and Epalinges. As an employer, UNIL encourages excellence, individual recognition and responsibility.

**Presentation**:
Recent progress in computer vision has accelerated the development of statistical downscaling, which uses statistical models to improve the spatiotemporal resolution of impactful climate variables, such as extreme temperatures, wind gusts, and precipitation. Machine learning (ML)-based super-resolution algorithms, which learn from data how to best generate high-resolution images from their low-resolution version, are gaining traction because of their improved accuracy and low omputational cost once trained. However, they are rarely designed to perform well on extremes, and their robustness is usually only tested in the present climate, where training data are available. These limitations prevent the widespread adoption of modern ML to better constrain uncertainties in the forecasting of local extremes and in the high-resolution projections of climate change.

For that purpose, we propose** two synergistic PhD projects on Machine Learning for Weather/Climate Super-Resolution**:
**The first project **generates spatially-resolved Swiss climate change scenarios that ensure physical consistency and preserve long-term trend.

**The second project **downscales medium-range forecasts over Switzerland, including small-scale features ignored by the original forecast.

In both projects, we will explore the added value of state-of-the-art ML for atmospheric science, which remains challenging to understand.

**Both PhD positions** will be hosted within UNIL’s recently established Expertise Center for Climate Extremes, and will include collaboration opportunities with research groups from the institutions listed below:
- MeteoSwiss
- LSCE-IPSL
- University of California, Irvine
- NVIDIA (Climate Simulation Team)

**Job information**:
Expected start date in position : 01.10.2024 / to be agreed

Contract length : 1 year, renewable to a maximum of 4 years.

Activity rate : 100%

Workplace : Lausanne Mouline (Géopolis)

**Your responsibilities**:
**Technical Responsibilities**:

- Specific to the climate projection project:_
- Integrate and test new algorithms for trend preservation in the downscaled variables, ensuring that long-term climatic changes are accurately reflected in the projections.
- Collaborate with MeteoSwiss to ensure that the downscaled models align with the ongoing updates to Swiss climate change scenarios and are informed by the latest observational data.
- Specific to the weather forecasting project:_
- Design and implement machine learning models for medium-range weather forecasting that can handle initial conditions significantly different from those seen in the training data, especially under extreme weather conditions.
- Ensure the calibration of downscaled outputs for longer lead times as predicted distributions shift towards climatology.
- Develop and test feature transformation techniques that enhance the generalizability and accuracy of forecasts across complex topography, such as the Alpine region.
- Collaborate with MeteoSwiss to ensure that the downscaled forecasts align with ongoing forecasting and post-processing needs.
- For both projects:_
- Over the course of 4 years, complete 3-4 semesters of teaching assistantship in geoinformatics, scientific programming, or machine learning for Earth and environmental science courses (no more than approximately 5 hours/week).
- Given that the collaboration with MeteoSwiss offers the opportunity to root your research in practice, we encourage flexibility between Lausanne and the MeteoSwiss offices (Zürich, Geneva, Locarno).

**Your qualifications**:

- Master’s degree in a quantitative field closely related to machine learning and/or climate science
- Strong background in scientific programming and data science
- Experience in manipulating scientific datasets, ideally including proficiency in Python.
- A solid foundation in applied mathematics and physics. We appreciate a range of competencies, including but not limited to calculus, differential equations, statistics, mechanics, and thermodynamics
- Strong communication skills in English
- Enthusiasm for both atmospheric/climate science and scientific machine learning
- (Preferred but not required) Experience in high-performance computing
- (Preferred but not required) Background in numerical weather prediction and/or climate change science
- (Preferred but not required) Teaching experience
- Note that proficiency in French and German is not required and free French and (Swiss) German courses are available at UNIL

**What the position offers you**:

- We offer a nice working place in a multicultural, diverse and dynamic academic environment. Opportunities for professional training, a lot of activities and othe

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