met-jobs@lists.reading.ac.uk
May 2019
Message 48

[Periods|Index by:DateThreadSubjectAuthor|Date:PreviousNext|Thread:(Previous)(Next)|List Information]

[Met-jobs] Postdoctoral Scientist in Atmospheric Dynamics / Machine Learning at ETH Zurich (2-year position)

From "Domeisen Daniela" <daniela.domeisen@env.ethz.ch>
To "met-jobs@lists.reading.ac.uk" <met-jobs@lists.reading.ac.uk>
Date Mon, 13 May 2019 18:34:04 +0000

ETH Zurich is one of the world’s leading universities specialising in science and technology. It is renowned for its excellent education, its cutting-edge fundamental research and its efforts to put new knowledge and innovations directly into practice. We invite applications for a Postdoctoral Scientist position at the Institute for Atmospheric and Climate Science.

Postdoctoral Scientist (2-year position, 100%)

Long-term weather predictions on weekly to monthly timescales are crucial for a wide range of sectors. While short-term weather prediction on time scales of several days is well established, methods for gaining predictability on weekly to monthly timescales are often more experimental and it remains unclear which methods might be the most valuable in dealing with long-term weather predictions. In particular, forecast skill over Europe decreases considerably after about a week, such that weather predictions beyond 2 weeks often exhibit little skill. Despite the limited skill of forecasts beyond this horizon, they are important for a range of applications, e.g. for crop planning, disaster readiness, and renewable energy. It is therefore necessary to better understand variability on these timescales and, with the advancement of machine learning algorithms and computational power, it is timely to use these methods to work on improving forecasts.

We are looking for a candidate for a 2-year Postdoctoral scientist position in the group of D. Domeisen at the Institute for Atmospheric and Climate Science at ETH Zurich. The project will address the predictability of the stratosphere on sub-seasonal to seasonal timescales, teleconnections, and the downward impacts of the stratosphere using machine learning (ML) methods. The aim is to advance our understanding of the classification, mechanisms, and predictability of stratospheric events and their surface impact. The Postdoctoral scientist will collaborate closely with the Swiss Data Science Center (SDSC) and the Department of Computer Science at ETH Zurich. 

The candidate is expected to have a PhD in atmospheric science or a related field, with a strong background in atmospheric dynamics, paired with a keen interest to develop their skills and knowledge of data science methods; excellent coding skills are essential. The candidate should speak English fluently, and have excellent communication and interpersonal skills. The candidate will be expected to be highly motivated to build a strong relationship, and to be able to work independently, with the SDSC.

We look forward to receiving your online application by June 15 including the following documents: CV, publication list, degree certificates, and names and contact information of two references. Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

For further information about the Institute for Atmospheric and Climate Science, please visit our website: www.iac.ethz.ch. For information about the research group visit www.iac.ethz.ch/group/atmospheric-predictability. For information about SDSC visit www.datascience.ch. Questions regarding the position should be directed to Prof. Daniela Domeisen by email at daniela.domeisen@env.ethz.ch (no applications).

More details and the online application can be found here:
https://apply.refline.ch/845721/7109/pub/1/index.html
Deadline: June 15, 2019

-----------------------------------
Daniela Domeisen, PhD
Assistant Professor for Atmospheric Predictability

ETH Zürich
Institute for Atmospheric and Climate Science
Universitätstrasse 16
8092 Zürich, Switzerland




Go to: Periods · List Information · Index by: Date (or Reverse Date), Thread, Subject or Author.