🇳🇱 De Bilt, Netherlands · 17h ago
De Rijksoverheid zoekt: Data scientist AI driven weather model
KNMI - Royal Netherlands Meteorological Institute
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Op deze pagina:Dit ga je doenDit krijg jeDit bieden we nog meerDit vragen wijHier kom je te werkenBijzonderhedenOver de functiegroepStel gerust je vraagDit ga je doenAre you passionate about advancing weather modeling using cutting-edge technology? Join the Royal Netherlands Meteorological Institute (KNMI) in the Destination Earth (DE) AI project (DE_376), where we push the future of high-resolution weather forecasts through deep learning. We are looking for a data scientist to help develop a model that matches the accuracy of our Harmonie-Arome model, especially for extreme weather. Collaborate with top meteorological institutes across Europe and shape the next generation of weather modeling.How You ContributeAs part of the Data Science cluster in the R&D Weather and Climate Models department, we are driving the development, maintenance, and application of several cutting-edge machine learning models. Our focus is on advancing ML methods for post-processing of numerical weather prediction (NWP) model output, as well as further developing data-driven weather models using machine learning – a task where you will play a key role. You’ll contribute to the further development of a stretched-grid weather model, leveraging a 40-year ERA5 re-analysis archive and multi-year high-resolution (km-scale) re-analysis data from several NWP models, such as our Harmonie-Arome model. This model is a graph neural network with a graph transformer.Additionally, you will compare the performance of the forecasts from the stretched-grid weather model with those from Harmonie-Arome.This exciting work is part of the DE_376 project. You’ll be collaborating with leading European meteorological institutes to develop high-resolution data-driven weather models for Europe with a focus on improving probabilistic forecasts of extreme weather by incorporating diffusion techniques a.o.Your activitiesYou develop and improve state-of-the-art machine learning models for data-driven weather forecasting.You contribute to the development of a stretched-grid weather model based on graph neural networks and graph transformers.You train and optimize models using large-scale weather datasets, including ERA5 and high-resolution NWP reanalysis data.You evaluate and benchmark machine learning weather forecasts against the Harmonie-Arome numerical weather prediction model.You collaborate with leading European meteorological institutes on the DE_376 project to advance probabilistic forecasting of extreme weather.You explore and apply innovative AI techniques, such as diffusion models, to improve next-generation weather prediction.Your teamYou will join the Data Science Cluster within the R&D Weather and Climate Models Department at KNMI. The cluster brings together data scientists and researchers who develop innovative machine learning solutions for weather and climate applications.In the Destination Earth (DE_376) project, you will directly collaborate with 2 other colleagues at KNMI, while also working with other leading European meteorological institutes. The team combines scientific excellence with an open and collaborative culture, where knowledge sharing and innovation are central.Would you like to know more? We understand that you might want to learn more about this position. Feel free to contact Maurice Schmeits, coordinator of the Data Science cluster, at +31 (0)6 15 64 89 02 or Ben Wichers Schreur, team leader, at +31 (0)6 81 33 68 49. They will be happy to assist you!KNMI's unique task is the gathering of information about the atmosphere and the subsurface and the translation of that information to risks for society.Would you like to learn more about how we strive to make our high-quality knowledge and information in the area of weather, climate, and seismology operationally available? You'll read more about it on our website.Dit krijg je Schaal 10 €3.496 - €5.535 (bruto) Arbeidsovereenkomst voor bepaalde tijd 15 maanden 36 uur Het salaris is gebaseerd op een werkweek van 36 uur. Dit bieden we nog meerAt our organization, you’ll work on meaningful projects that make a real impact in the Netherlands. In a workplace that values your professional growth and recognizes your contributions. Our benefits reflect that commitment.Core benefits that provide security:144 hours of annual leave per year (based on a 36-hour work week)full public transport reimbursement or a mileage allowance of € 0,07 or € 0,23 per km (conditions apply)mobile phone and laptop€ 750 budget for setting up your home workspace, plus a daily homeworking allowance of € 2,45pension scheme through ABPA supportive environment that encourages growth:hybrid working options tailored to your role, in consultation with your managerpersonal development opportunities, training and career coachingFlexible benefits designed around your life:Individual Choice Budget (IKB): use this budget (the amount is 16.5% of your salary) for options such as repaying student loans, purchasing a (electric) bike with accessories, company fitness, making your home more sustainable, or other tax-friendly purposesflexible leave arrangements, including care leave, parental leave, adoption leave, and foster care leaveour program provides support for visas and related matters, and language training (it does not include accommodation)You can find the full Dutch government collective labor agreement (CAO Rijk) here.Dit vragen wijYou hold an MSc in data science or a related field, with a strong focus on machine learning.You have hands-on experience developing deep learning models using PyTorch.Experience in weather forecasting (models) is a bonus.Experience with a data-driven weather model (like AIFS of ECMWF) and/or Anemoi is a bonus.We are looking for someone who demonstrates the following competenciesAnalytical skills: you are good in interpreting and analyzing scientific results.Collaboration: you enjoy working in multidisciplinary and international teams.Results orientation: you focus on delivering high-quality outcomes and achieving project goals.Innovation: you are eager to explore and apply new machine learning techniques.Communication skills: you communicate complex ideas clearly, both verbally and in writing.Hier kom je te werkenOver Koninklijk Nederlands Meteorologisch InstituutRoyal Netherlands Meteorological Institute (KNMI) The weather is temperamental, the ground moves and the climate changes. For our safety and prosperity, we need to know what risks and opportunities this brings. And: how we can best prepare ourselves. The Royal Netherlands Meteorological Institute (KNMI) is the national knowledge and data center for weather, climate and seismology. Reliable, independent and focused on what the Netherlands needs. For a safe Netherlands that is prepared for the impact of weather, climate and earthquakes.We use our core values - Development, Cooperation and Relevance - to achieve our ambition, both within and outside KNMI, nationally and internationally. We develop our knowledge and expertise and work together to create a single KNMI that makes a difference to society!Organizationally, KNMI falls under the Ministry of Infrastructure and Water Management.The Ministry of Infrastructure and Water Management (IenW) is committed to a safe, accessible and livable Netherlands. That is why the Ministry is working on powerful connections by road, rail, water and air. And IenW protects against flooding, ensures the quality of air, water and soil and the realization of a circular economy.Talent as the basis, diversity as the strength The KNMI is an inclusive organization. An organization that provides space for everyone and uses the strength of its diverse workforce to achieve better results together for the Netherlands. Inclusive means that everyone feels involved and valued; not in spite of their differences, but thanks to them. KNMI: R&D Weather and Climate modelsThe department Research and Development of Weather and Climate models (RDWK) investigates andSourced from LinkedIn. Relocantly aggregates public job postings; apply on the original site.