机器学习/大气科学：PhD and Postdoc at BFH and TU Delft
学术
2024-11-13 19:00
荷兰
PhDs and Postdoc: AI for Earth & Climate at BFH and TU DelftWe offer 2 PhD positions and 1 postdoc position in European research project UrbanAIR at the interface of machine learning, Earth observation atmospheric sciences. We are looking for motivated and proactive doctoral and postdoc researchers interested in machine learning, Earth and Climate science, including remote sensing, explainable AI, generative and foundation models, hybrid and physics-based modelling.Desired start date is 1 January 2024 (may be adjusted by mutual agreement). The PhD student salaries correspond to around 50'000 CHF or 54'000 € per year. The PhD positions are 4-year full-time positions and fully-funded. The PhD student will be embedded at the Department of Geoscience and Remote Sensing at TU Delft, where the student is set to receive the PhD degree. The postdoc position is for one year with possibility of extension based on mutual agreement. Hybrid working is possible.- MSc degree (or equivalent) in physics, computer science, atmospheric or geoscience, engineering, applied mathematics, or a related field
- Experience in machine learning, deep learning, statistics, image processing, signal analysis
- Interest in remote sensing and atmospheric modelling
- Excellent analytical and quantitative skills, data analysis, scientific modelling and interpretation skills
- Strong programming skills incl. Python
- Good communication, writing and presentation skills will be advantageous
- You are self-motivated and proactive, enjoy working with complex topics and thrive on challenges
- You are committed to your project, enjoy collaborations and are a good team player
- For one of the PhD positions, prior experience with computational fluid dynamics and large eddy simulations will be advantageous
- Conduct research on topics related to this project
- Publish your results in peer-reviewed international journals and at conferences
- Collaborate with a team of international researchers
- Organize project activities, engage in project meetings, report project work and results
- You will have the opportunity to assist in teaching and co-supervise undergraduate students; as a postdoc researcher, you will have the opportunity to co-advise PhD students. (optional)
You will be embedded in the Energy and AI group at TU Delft and BFH. Our group focuses on understanding and predicting weather- and climate-related factors that affect renewable energy resources and ambient conditions, particularly from solar radiation and wind, enabling decision support. Our work includes the development of machine learning techniques and analysis of atmospheric and Earth observation data in this field, and investigating processes for forecasting and optimising solar and wind energy resources. Our research contributes to advancing the transition to low-carbon energy systems.- Engage in cutting-edge machine learning / AI to facilitate the energy transition and address pressing challenges related to climate change, societal and energy topics
- Interdisciplinary research projects combining machine learning with Earth observation and atmospheric/climate research
- Make use of high-performance supercomputing infrastructure
- Opportunity to co-advise undergraduate students and PhD students (postdoc) and opportunity to lecture (optional)
- A friendly, international, dynamic work place
- We are committed to diversity
- Competitive salary (approx. 54'000 € p.a. for PhD students and 100'000 € p.a. for Postdoc researcher depending on experience)
- Travel to conferences, collaboration and publication fees covered
Further information about the Horizon Europe project UrbanAIR can be found on https://www.urbanair-project.eu/. More information about our research and group can be found on https://www.angela-meyer.net/.To apply, please submit your CV, BSc/MSc transcripts and theses, names and contact details of 3 references, your Github profile and publications (if any), your PhD thesis (postdoc only). Applications are reviewed on a rolling basis until the positions are filled.![]()
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