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-edge optical microscopy systems for biomedical applications. This project involves the development of compact, label-free quantitative tomography systems and inverse scattering algorithms to push the
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experimental settings. In addition to fieldwork, the PhD candidate will contribute to the development of novel inversion algorithms for EMI and GPR based on full-waveform inversion techniques. These methods aim
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algorithms from technical sources (e.g., papers, notes, and group discussions) in close collaboration with group members. The work is primarily algorithmic scientific software development: implementing and
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retrieval algorithm development with focus on using the polarimetric signals, the new FIR or sub-mm bands, and/or the ML/AI approach; (3) ML/AI application on system/pattern tracking on satellite images
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features from multiple imaging modalities (CT, MRI, PET, ultrasound); (2) design advanced AI algorithms for early-stage cancer detection with high sensitivity and specificity; (3) create user-centric AI co
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 11 days ago
technologies and algorithms Collaborate with researchers at ANU, Monash, and The University of Melbourne The Position The Research Fellow will contribute to the ARC Discovery Project “Seeing through Space and
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Join the forefront of groundbreaking research at City of Hope where we're changing lives and making a real difference in the fight against cancer, diabetes, and other life-threatening illnesses
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
the accuracy, reliability, and lead time of flash flood early warnings across different geographic and climatic contexts. · Integrating and implementing scientific algorithms on high performance and
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are to be identified, designed, and integrated into ASSUME. One focus of the thesis is on linking different levels of analysis within the simulation. On this basis, various tariff and bidding approaches
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next-generation intelligent systems that are both scalable and explainable. This role bridges algorithmic research and systems implementation, offering opportunities to collaborate with leading academics