Energy állások Külföldön (programozó, fejlesztő)

• Performance analysis of user codes and guidelines for optimization
• Benchmarking time-to-solution and energy-to-solution
• Optimizing HPC operational parameter towards energy efficiency
• Assessment of performance portability
• Guiding scientists through the optimization process

• You will familiarize yourself in detail with the implementation and structure of existing in‑house software tools.
• You will contribute to the conceptual development and software implementation of methods for load processing and fatigue analysis for various applications of our large‑diameter bearings.

• You implement numerical simulation methods and their applications for large scale power systems.
• You develop high performance algorithms for dynamic simulation taking also advantage of hardware-based acceleration techniques such as FPGA or GPU.

As a research engineer, you will be at the forefront of pioneering system design, with the responsibility to architect, develop, and deploy sophisticated AI systems that seamlessly integrate reinforcement learning with agentic AI systems and multi-modal foundation models....

As a Research Engineer, you will develop cutting-edge Vision-Language-Action (VLA) architectures that empower AI agents to interpret human instructions and act autonomously in complex environments....

• Design and implement modern front-end interfaces for energy performance dashboards and AI-driven applications
• Develop interactive visualizations and compelling data storytelling components for business stakeholders

• conceptualize and set up the spatial reconstruction framework based on generative AI
• develop and benchmark AI-based methods (GAN, diffusion, transformer) and standard interpolation techniques (graph/spatiotemporal interpolation, kriging/GPs)

• Developing parallel and distributed algorithms for processing ultra-large 3D images across multiple GPUs
• Designing and adapting scalable deep learning models for segmentation and landmark detection (e.g. U-Net variants, Transformers)

• Developing multi-scale and multi-modal representation learning methods for scientific imaging data (e.g., SEM, TEM, EBSD).
• Learning representations that are robust to scale changes, modality shifts, and domain differences across instruments and laboratories.