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Field
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techniques. You will have the opportunity to participate in various projects utilizing artificial intelligence (AI) and machine learning (ML) to develop applications that optimize combat casualty care
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) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g., UR5, Franka Emika
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focused on using spatial profiling and machine learning of human specimens in combination with functional experiments in animal models to understand cancer initiation, progression, and metastasis. We
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of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical systems. Key Responsibilities Derive and analyse closed-form mathematical
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knowledge of deep learning architectures and, in particular, Large Language Models (LLMs). Knowledge is valued both in advanced prompt engineering techniques (e.g. few-shot learning, chain-of-thought
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focuses on developing cutting-edge statistical/machine learning methods for fitting complex network models to partially observed hospital infection data, leveraging patient movement data. This research will
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chip packaging F5 Experience using data analysis or machine learning tools for electronics reliability or sustainability assessment F6 Experience with rapid prototyping technologies (3D printing, laser
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combination with data science approaches such as machine learning and data assimilation via cryospheric models. A main focus of this work is snow and glaciers in the mountains around the globe. Candidates with
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using NVIDIA Isaac Sim, and learning-based policy development for manipulation and locomotion tasks. The role will involve both experimental robotics work and AI model development for sim-to-real humanoid
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, selectivity and phenotype mechanisms directly in primary human cells and employ machine learning methods for data analysis. This includes activity-based profiling, global- and phosphoproteomics and phenotype