Research Intern - GHAI-2

Job Description Research Intern - GHAI-2 Job Location: Santa Clara, California Location Flexibility: Multiple Locations in Country Req Id: 8046 Posting Start Date: 5/8/26 At Fujitsu, we are driven by our purpose to make the world more sustainable by building trust in society through innovation. We have been a pioneer in technology and innovation for over 80 years, and we are committed to using our expertise to help businesses and organizations transform for the digital age. We believe that digital transformation is essential to creating a more sustainable future. That's why we are working with our customers to develop solutions that can help them reduce their environmental impact, improve their efficiency, and create a more equitable society. We are committed to contributing to the United Nations Sustainable Development Goals (SDGs). These goals are a blueprint for a better future for all, and we believe that technology can play a vital role in achieving them. If you share our passion for making a meaningful impact on the world, we invite you to join our global family of 130,000 employees spanning more than 50 countries. We are a diverse workforce, and we offer a wide range of opportunities for you to grow and develop your career. Together, we can create a more sustainable future for all. Research Intern at Fujitsu Research of America Location: Santa Clara, CA The Space Data Frontier Research Center at Fujitsu focuses on combining research across multiple technical fields to address large-scale societal and industrial challenges. These challenges require new approaches that integrate artificial intelligence, sensing, modeling, optimization, and domain knowledge. Our goal is to develop evidence-based scientific tools that support decision-making, improve operational efficiency, and accelerate innovation across industries. We are seeking exceptional research interns to join our Space Data Frontier Research Center Lab in Silicon Valley to work on novel problems and applications related to wind field predictions. You will contribute to developing machine learning based models for predicting wind fields for application to complex real-world systems and scenarios. The internship duration is 3 months. Job responsibilities Develop and train machine learning models for wind field prediction using diverse data sources, ranging from numerical model outputs to meteorological and atmospheric observations. Incorporate data assimilation techniques into ML pipelines to improve forecast accuracy and physical consistency. Explore and integrate satellite data into the modeling pipeline to enhance spatial coverage and resolution. Benchmark developed models against established baselines and conduct rigorous evaluation across diverse real-world scenarios. Contribute to publishable research suitable for top-tier venues such as NeurIPS, ICML, or related conferences and journals. Collaborate with research scientists and domain experts to translate model outputs into actionable insights for complex industrial and societal applications. Requirements Currently enrolled in a PhD program in a relevant area such as computer science, mechanical engineering, environmental science or engineering, aerospace engineering, atmospheric sciences, or applied mathematics. Strong programming skills in Python and experience with machine learning or scientific computing libraries such as PyTorch, JAX, NumPy, SciPy, or related tools. Prior experience with data assimilation methods; whether traditional (e.g., Kalman filtering, variational methods, ensemble approaches) or modern learning-based and generative techniques, applied to physical or geospatial systems. Demonstrated experience with deep learning architectures for physical or spatiotemporal systems, such as diffusion models, generative AI, or neural operators applied to scientific problems; experience with physics-informed generative modeling or hybrid physics-ML approaches is a huge plus. Experience integrating satellite data, remote sensing products, or large-scale geospatial datasets into ML pipelines is a plus. Background in fluid dynamics, atmospheric modeling, numerical weather prediction, or related physical sciences is a plus. Excellent written and verbal communication skills, and the ability to collaborate across interdisciplinary teams. Fujitsu salaries are aligned to the specific geographic location in which the work is primarily performed. It is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the circumstances of each situation. The pay range for this role takes into account the wide range of factors that are co