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materials. 4. An established network for collaboration in materials characterization, device fabrication, and application development. 5. Independence, initiative, and strong time-management, planning
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multidisciplinary research integrates molecular biology, pharmacology, genomics, computational biology, and translational oncology to identify actionable signaling networks and develop predictive biomarkers
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collaborative research partnerships and stakeholder networks experience supervising, mentoring, or supporting students and/or early career researchers experience with data science approaches to analyse clinical
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Research Center for Molecular Medicine (CeMM), ÖAW | Graz 12 Bez Andritz, Steiermark | Austria | 3 months ago
. Benefit from an international research environment, strong team spirit, excellent scientific networks and support for relocation where applicable. Competitive salary in the range of €5.014,30-5.499,10 EUR
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culture. Diversity is positively encouraged, through our EDI Committee, working groups and networks, for example eng.ox.ac.uk/women-in-engineering, as well as a number of family friendly policies.
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new Austrian Academy of Sciences institute dedicated to molecular and computational metabolism; Benefit from an international research environment, strong team spirit, excellent scientific networks and
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international research proposals. Collaborate across disciplines within Mission Mjøsa and relevant external research networks. Strengthen NTNU’s position within digital twin research, semantic interoperability
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
researchers who undertake high impact collaborative and cross-disciplinary research, and whose expertise in AI-driven wireless communications, semantic processing, and 6G network architectures to contribute
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, recovery and separation of supernatant, and drying process. Support AI‑enabled data analysis, including collaboration on Artificial Neural Networks and Gaussian Process modelling, to accelerate processing
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knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and