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Field
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-performance computing resources suitable for large-scale machine-learning and foundation-model experiments. Your role We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025
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Science Statistics / Biostatistics Applied Mathematics Data Science Demonstrated expertise in modern machine learning, including at least one of the following: Deep learning (e.g., transformers, sequence models
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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. Teaching skills. Additional assessment criteria: A strong ability to develop and conduct high-quality research independently. Experience using deep learning methods and computer vision with biological data
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understanding and generation, media forensics, anomaly detection, multimodal learning with an emphasis on vision-language models, computer vision applications for space. Key responsabilities: Shape research
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–functional modeling of root system architecture. Phenomics data integration and high-dimensional trait analysis. Predictive breeding and quantitative genetic modeling. Machine learning approaches to genotype
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. Required PhD in Computer Science / AI / Machine Learning Strong publication record in AI, ML systems, or related areas Strong programming skills in Python, C/C++ and experience with PyTorch, TensorFlow, JAX
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in core methods of machine learning/artificial intelligence. ● Experience with data warehousing and building large, curated datasets with protected health information, suitable for training large
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Publish high-impact research in leading journals and present findings at international conferences on energy systems and machine learning Collaborate with industry partner to tackle challenges of practical
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Swarm Intelligence, Reinforcement Learning and Optimization Techniques. As a Postdoctoral researcher, you will: Lead cutting edge research in Swarm Intelligence and Machine Learning, addressing challenges