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function differentiation, compositional Bayesian inference techniques); analyzing what is required (e.g., choice of data structures, static analyses and compiler optimizations, parallelism and concurrency
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domains: historical documents and Earth imagery. For example, from temporal series of multispectral Earth images, we will identify types of moving objects, areas with different types of vegetation
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processes related to carbon cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset Ability to work independently and cooperatively as part of an interdisciplinary
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BMAD simulation tool, an advanced open-source particle tracking framework, the doctoral candidate will perform start-to-end simulations, benchmark physical models, and explore tolerance studies. Specific
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cell-fate tracking tracking and studying various stages of the metastatic cascade, we set out to follow tumour cell-niche interactions to reveal how distant sites shape cancer progression-and where we
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Department at Monash has a thriving research culture, and is ranked 33rd in the world in the latest QS rankings. Candidate Requirements The successful applicant will have an excellent academic track record in
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(DPPs) has become a regulatory priority. These passports are envisioned to revolutionize the way products are tracked and evaluated, providing detailed information about their materials, manufacturing
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creative – You bring substantial knowledge of statistical (e.g. Bayesian) methods, strong analytical skills, and creativity. Programming skills – You are proficient in Python and/or Matlab. Research
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system using deep learning (DL). The project’s objectives include generating training data from synthetic datasets and real-world images (cadaver and actual intraoperative THR images), developing a marker
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exposure, and thermal fluctuations. Key Objectives: - Develop and characterize low-carbon materials with multi-functional properties - Assess mechanical performance and long-term durability under simulated