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collaboratively. Desirable skills Experience in training or fine-tuning large-scale models (LLMs, VLMs) in distributed settings. Familiarity with cluster computing environments (e.g., SLURM) and Linux-based
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Declaration of interest regarding PhD project within the field of biomarker and therapeutic targe...
. The PhD student will work with patient cohort to perform biomarker analyses and statistical modeling of clinical outcomes. In parallel, the student will contribute to the development and validation of novel
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systems Experience in deep learning, computer vision, or multimodal data integration Exposure to federated learning, privacy preserving analytics, or distributed systems Knowledge of clinical data models
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learning Distributed and federated training The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another
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- Quantum Reinforcement Learning, quantum computing, QKD - quantum key distribution, entanglement distribution System-level design and optimization AI & Intelligence: Agentic AI, Edge AI, information
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will investigate the distribution, sources, and characteristics of micro- and nano-plastics in hadal sediments. The work will combine deep-sea sediment sampling with advanced analytical approaches
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principles that govern product distribution and catalytic performance. Close collaboration with theory will be central to guiding and rationalizing the experiments, while synchrotron-based techniques will
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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at the University of Oldenburg and involving national and international partner institutions. As a diverse and international team of scientists from biology, physics, chemistry, computer sciences and social sciences
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position in the area of machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement