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Field
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Language Models (MLLMs) and their agentic implementations. Develop and benchmark novel adversarial attacks and defense strategies, focusing on the intersection of computer vision, natural language processing
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managing all underlying compute, networking, and storage resources (e.g., Kubernetes clusters, GPU instances). Feature Engineering: Define shared data and feature management patterns to ensure consistency
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/CloudFormation) for provisioning and managing all underlying compute, networking, and storage resources (e.g., Kubernetes clusters, GPU instances). Feature Engineering: Define shared data and feature management
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: Experience in real-time simulation hardware like Opal-RT and RTDS. Experience with software development. Experience with use of DSPs, FPGAs, advanced computing (e.g., GPUs, QPUs). Excellent written and oral
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to apply; we value employees with a willingness to learn. Understanding of technologies employed in research in higher education Familiarity with distributed computation solutions Familiarity with GPU
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qualifications In this project, we advance an AI-driven T cell-targeting strategy based on de novo-designed peptide-MHC (pMHC) binding proteins. Using computational protein design, we create synthetic recognition
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- Regular Salaried Payroll Designation PeopleSoft Seniority Unit Terms of Appointment Staff - 12 month Position Pension Eligibility ABP Qualifications Minimum Education and Experience A PhD degree in Computer
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to apply; we value employees with a willingness to learn. Understanding of technologies employed in research in higher education Familiarity with distributed computation solutions Familiarity with GPU
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computing environments for advanced research. B2 Knowledge of scaling and optimising software to take advantage of GPU / HPC infrastructure. Skills Essential: C1 Excellent written and verbal communication
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Max Planck Institute of Animal Behavior, Radolfzell / Konstanz | Konstanz, Baden W rttemberg | Germany | about 2 months ago
Behavior (CASCB), providing access to shared infrastructure, high-speed data networks, and central technical services. Computational needs will be met through multiple layers of resources: local GPU-equipped