Lead AI Research Scientist - Subseasonal to Seasonal (S2S) Weather PredictionWhat we offerA versátil schedule with up to 80% remote work, based in Barcelona.A permanent, full-time contract.€40,000 – €50,000 gross per year, depending on experience.Time and encouragement to publish your work and present it at scientific conferences.Ownership of a strategic research line from day one, working directly with our CTO.The opportunity to work with ground-breaking technology and create the future of subseasonal-to-seasonal prediction.High-impact, ambitious projects with real learning opportunities, in a company that respects your time and supports healthy work-life balance.The roleWe are looking for a PhD scientist to lead Nebbo's next – and most ferocious – research line: the design of afully AI-based model for long-range weather prediction, covering horizons from day +15 to the coming months and years (subseasonal, seasonal and beyond).This model will feed our existing products alongside the physics-based NWP models that already power them, and it is one of the most promising ways of enablingNebbo's expansion into the energy trading sector. You will own this research line from day one, reporting directly to our CTO and working closely with our technical team.RequirementsPhD (already awarded)in physics, applied or computational mathematics, atmospheric, climate or Earth-system science, computer science, AI or engineering.Ideally, 2 years of post-PhD experience(postdoc or industry). Your PhD and/or your most recent work should revolve around ML/DL models for weather or climate prediction at long ranges – subseasonal, seasonal or longer.Proficiency inPythonand deep learning frameworks (PyTorchor similar).Proven experience developingDL architectures for large-scale weather forecasting (graph-based networks, physics-informed ML, transformers, diffusion models…), and hands-on knowledge of state-of-the-art AI weather models such as GraphCast, GenCast,



AIFS, FuXi-S2S or Pangu-Weather.Experience identifying and analysingpredictability sources and modes of variability in the Earth system.Experiencepre-training models on reanalysis dataand working with the Earth-science data stack: ERA5, S2S/C3S hindcasts, xarray, zarr, dask, NetCDF/GRIB.Experiencetraining and optimising models with experiment tracking tools(e.G.MLflow).Solid background inprobabilistic forecasting and verification: ensembles or generative approaches, CRPS, reliability and skill scores against climatology.Experience inmodel evaluation and continuous validation & monitoringof deployed models.Fluent professionalEnglish, and the ability to explain complex results to customers and non-expert stakeholders.The right to work in Spain (or the ability to obtain it on your own), and being based in – or willing to relocate to – the Barcelona area.Start date and fundingHiring for this position iscontingent upon Nebbo securing public fundingthrough the Neotec(CDTI) and/orTorres Quevedo(Spanish State Research Agency, AEI) programmes.Neotec:a decision is expected inOctober–November 2026. If Nebbo receives this funding, you could join us at that point.Torres Quevedo:otherwise, the start date will depend on the Torres Quevedo resolution, expected insecond quarter of 2027. If you are selected, we will propose you as NebboWhat you will buildPhase 1 – Regional S2S model.Develop an AI model for subseasonal forecasting at country or electricity-market bidding-zone level,



for a small number of regions.Phase 2 – Global S2S engine.Research and develop a worldwide AI-based engine for subseasonal-to-seasonal predictions.Your responsibilitiesDesign, develop and train deep learning architectures for large-scale weather forecasting (graph-based networks, transformers, generative and physics-informed ML).Identify and analyse sources of predictability and modes of variability in the Earth system (e.G. ENSO, MJO, NAO, the stratospheric polar vortex, soil moisture, sea ice) and exploit them in the models.Pre-train models on reanalysis data (e.G. ERA5) and fine-tune them with hindcasts and observations.Train and optimise models with a rigorous, reproducible workflow using experiment tracking tools (e.G. MLflow).Evaluate models against climatology and state-of-the-art dynamical forecasts, and set up continuous validation and monitoring of deployed models' performance.Work with our engineering team to bring models into production.Interact with customers and other stakeholders, turning their needs into forecast products.Represent Nebbo at fairs, workshops and scientific conferences.Help shape Nebbo's future in AI-based modelling and in the energy trading sector.About NebboNebbo is a Barcelona-based startup, born in 2023 as a spin-off of the Vortex group, a world leader in modelled wind and weather data. We deliver subseasonal-to-seasonal (S2S) forecasts – from a few weeks to several months ahead – that combine physics-based numerical weather prediction (NWP) models with AI, helping companies in energy, agriculture and infrastructure plan with confidence. Our motto says it all:see further, plan better.Nice to haveExperience withcloud-based model trainingand deployment to aproduction model registry, ideally on Google Cloud Platform (GCP).Experience in theenergy sector, ideally in renewable energies.Publicationsin peer-reviewed scientific and technical journals.#J-18808-Ljbffr

📌 Ai Research Scientist — Generative Ai & Vision (Flexible Europe) (Eixample)
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📍 Eixample