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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design
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implementation and commissioning Knowledge of machine learning or reinforcement learning applied to control systems is a strong asset Experience with accelerator technology, specifically photocathodes is an asset
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for candidates with: A strong BSc and MSc degree in computer science, machine learning, mathematics, physics, engineering, or a related field Strong foundations in machine learning and deep learning Experience
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Cognitive Science, Medical Informatics, or a comparable quantitative discipline Proven hands-on experience working with LLMs, foundation models, multimodal AI systems, or related machine-learning technologies
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Knowledge of machine learning, Large Language Models (LLMs), Vision Language Models (VLMs), or generative AI Experience with Retrieval-Augmented Generation (RAG), AI agents, model-driven engineering, DevOps
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influencing factors and improve the accuracy, robustness and energy efficiency of intelligent sensing systems. Apply AI as an engineering tool: Use signal processing, statistical methods and machine learning
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limited time until 31st of December 2032, available immediately Key Responsibilities: Design, implement and refine automated image analysis workflows, including machine learning–based methods
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, Mathematics, Physics, Engineering, Computer Science or related disciplines with a strong academic record. Strong background in data analysis, statistics, machine learning, scientific computing, or related
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to design and control biological systems in a targeted manner. An integral part of BioSysteM is the AI Core Unit, a cross-institutional support unit. This unit aims to make machine learning (ML) and
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Max Planck Institute for Biological Cybernetics, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 1 month ago
interacts with the prefrontal cortex to integrate interoceptive signals and guide higher-order cognition. Combining ultra-high-field layer fMRI at 9.4 Tesla, diffusion MRI, and machine learning in humans with