PhD position in Advanced Deep Data-Driven Nowcasting Models (1.0 FTE)

Meteorological services worldwide experience an ever-increasing demand of nowcasts, i.e., forecasts with short lead-times with a high temporal and spatial resolution, and a high update frequency. Nowcasting tremendously impacts the socioeconomic needs of many industrial sectors which rely on weather-dependent decision-making. Furthermore, nowcasting applications have found their way to the broad public, making it a ubiquitous feature of modern, industrialized societies as is used for planning, organization and management of a wide range of both personal and economic aspects of life. Therefore, making accurate nowcasting is a crucial factor in many weather-dependent systems (such as in modelling energy consumption, power load forecasting, traffic networks, Renewable Energy, environmental modelling) for cost savings, efficiency, health, safety and organizational purposes.
To date, the primary method for weather forecasts is numerical weather prediction (NWP). NWP relies on mathematical models that consider different physical properties of the atmosphere such as air velocity, pressure and temperature. The NWP-based models can generate accurate weather predictions of several hours to days into the future. However, they involve solving highly complex mathematical models which are computationally expensive and require enormous computing power and thus usually are performed on expensive super computers. Due to their high computational and time requirements, NWP models are less suitable for short-term forecasts.
Recent advances in Artificial Neural Network architectures (ANNs) have enabled data-driven based models to bridge the present gap for short-term forecasting. The research in this PhD project will be on developing a new theory to achieve more reliable and accurate nowcasting deep learning models. The key idea is to leverage large amount of unlabelled data for learning meaningful representation, incorporating several atmospheric variables and equipping the models with uncertainty quantification. The focus of this PhD project will be on developing novel self-supervised learning models to push the boundary of current deep data-driven nowcasting models.
You will join the department of Information and Computing Sciences where a team of enthusiastic researchers is developing new theories for applications in machine learning, control and biomedical signal processing.
This is a PhD position for 5 years, which includes research as well as teaching. You will spent approximately 30% of your time on varying teaching support activities. We offer the opportunity to take significant steps towards acquiring a basic teaching qualification (BKO), which qualifies you as a teacher in the Dutch higher education system.

We are looking for a motivated candidate to join our team. You are equipped with a critical mindset and recognise yourself in the following qualifications. You have:

• completed a relevant MSc degree in an applied sciences field relevant to the PhD research, i.e. Engineering, Computer Science, Statistics, Applied Mathematics;
• affinity with Machine Learning and Deep Learning Research;
• a strong mathematical background in (numerical) linear algebra, statistics and optimization;
• good programming skills in Python, TensorFlow, PyTorch or Keras.

• A position for 5 years;
• A full-time gross salary ranging from € 2541 to €3247 in scale P;
• 8% holiday bonus and 8.3% end-of-year bonus;
• A pension scheme, partially paid parental leave, and flexible employment conditions based on the Collective Labour Agreement Dutch Universities.

In addition to the employment conditions from the CAO for Dutch Universities, Utrecht University has a number of its own arrangements. These include agreements on professional development, leave arrangements and sports. We also give you the opportunity to expand your terms of employment through the Employment Conditions Selection Model. This is how we encourage you to grow.
Are you an international applicant? Our International Service Desk can prepare your stay (visa application etc.) and help you in case of questions regarding living in the Netherlands .
For more information, please visit working at Utrecht University .

A better future for everyone. This ambition motivates our scientists in executing their leading research and inspiring teaching. At Utrecht University , the various disciplines collaborate intensively towards major societal themes. Our focus is on Dynamics of Youth, Institutions for Open Societies, Life Sciences and Sustainability.
At the Faculty of Science , there are 6 departments to make a fundamental connection with: Biology, Chemistry, Information and Computing Sciences, Mathematics, Pharmaceutical Sciences, and Physics. Each of these is made up of distinct institutes that work together to focus on answering some of humanity’s most pressing problems. More fundamental still are the individual research groups – the building blocks of our ambitious scientific projects. For an impression, watch Working at the Faculty of Science .
The Department of Information and Computing Sciences is nationally and internationally renowned for its fundamental and applied research in computer science and information science. In our constantly changing (digital) society, the Department of Information and Computing Sciences is constantly looking for new, realistic ways to push the boundaries of both science and social application. We contribute to innovative information technologies through the development and application of new concepts, theories, algorithms, and software methods. Relevant areas of interdisciplinary research include Game Research, Foundations of Complex Systems, Applied Data Science, and Artificial Intelligence.

If you have any questions that you’d like us to answer, please contact Siamak Mehrkanoon (assistant professor in the Department of Information and Computing Sciences) via s.mehrkanoon@uu.nl.
Do you have a question about the application procedure? Please send an email to science.recruitment@uu.nl .
For more information, please visit working at the Faculty of Science . 

Everyone deserves to feel at home at our university. We welcome employees with a wide variety of backgrounds and perspectives. If you have become enthusiastic about this role, then simply respond via the "Apply" button!
Please enclose: 

• your letter of motivation;
• your Curriculum Vitae;
• copy of your MSc diploma and transcripts;
• copy of your MSc. Thesis;
• list of accepted or submitted publications, if any;
• names, telephone numbers, and email addresses of at least two references.

If you are within a few months from the completion of your MSc degree, we also welcome you to apply! In that case, please enclose a letter from your MSc thesis supervisor indicating when you are likely to graduate.
If this specific opportunity isn’t for you, but you know someone else who may be interested, please forward this vacancy to them.
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