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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 6 days ago
changing world. We believe that diverse perspectives drive innovation. Through strong partnerships, we accelerate the transfer of new ideas from the lab to real-life applications, improving lives
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innovative deposition processes and high-throughput materials screening. The opportunity Contribute to groundbreaking research on accelerated thin-film and process development for emerging applications such as
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: Develop AI-assisted experimental workflows for oxide semiconductor deposition, aiming to accelerate process optimization and unravel new process–property relationships, and contributing to an EU Horizon
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that enable dozens of molecular factors to be screened in vivo during development and disease, accelerating the discovery of pathways that control immune-cell behavior. You will help expand these technologies
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About the Role We are seeking a full-time Postdoctoral Research Associate, funded through a major international research programme dedicated to accelerating Parkinson's disease research
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pride in being an inclusive community of outstanding learners, investigators, clinicians, and staff where interdisciplinary collaboration is embraced and great ideas accelerate translation of fundamental
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courses for undergraduates in composition and communication (CIS 110 Composition and Communication I, CIS 111 Composition and Communication II, CIS 112 Accelerated Composition and Communication, as
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more informed and representative decisions, accelerate crop improvement, and strengthen food security and climate resilience across the Global South. The successful candidate will join the Biometrics
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innovative disease models that improve our understanding of disease triggers, identify novel biomarkers, and accelerate therapeutic discovery. In this role, you will lead a research project focused on
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extracting transport properties from molecular dynamics trajectories. ● Experience with GPU-accelerated machine learning frameworks (for example CUDA, PyTorch, or GPU-enabled LAMMPS). ● Experience