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Field
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to contributions in top-tier international journals and real-world implementations. Responsibilities: Develop and refine algorithms for decision optimization and risk control, utilizing big data and advanced
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learning (ML) for high-fidelity data ‘stitching’. The integration of data from multiple analytical platforms is critical for advancing the understanding of complex biological and chemical systems. This work
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, localization, and sensing, with a focus on developing next-generation multiple-antenna systems while optimizing overall system performance. As a doctoral student, you devote most of your time to doctoral studies
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, modeling, and analysis for the SCHOLAR project. Develop and deploy the SCHOLAR dashboard, incorporating CHW feedback through multiple design iterations. Collaborate with CHWs and senior research personnel
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that are relevant to industry demands while working on research projects in SIT. The primary responsibility of this role is to lead the research and development of a secure and privacy-preserving multimodal biometric
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will apply state-of-the-art machine learning algorithms and custom disease-relevant genomic datasets (e.g., coronary artery single-nucleus chromatin accessibility and RNA sequencing) to develop targeted
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creative ideas in supportive environments. The clinical infrastructures include multiple imaging scanners, including a long axial field-of-view (LAFOV) PET/CT scanner, and aims to push the limits of modern
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slicing. - Develop advanced AI/ML algorithms and data analytics techniques to automate and optimise exposure requests, adapted to available resources and real-time demand. - Propose and
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scientists, nuclear medicine physicians) to develop and implement innovative AI algorithms applied to medical images To lead effort on enabling translational and physician-in-the-loop AI solutions for medical
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technological progress in our increasingly digital, data-driven world. Researchers in Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By