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Research Infrastructure? No Offer Description TASKS/ROLE * conducting research under the project Design-ready forward and inverse surrogate modeling of high-frequency structures using deep learning and
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qualifications PhD in Microbiology, Computational Biology, Genomics, Systems Biology, Statistics, Machine Learning or related discipline Publication record in Microbiology, Metagenomics, Metabolic/Statistical
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by integrating petrophysics, rock physics, geophysics, geomechanics and machine learning. A detailed project plan will be developed in collaboration with the successful candidate at the start of PhD
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Machine Learning / Deep Learning / LLMs Other qualifications For the doctoral programme in question, the following are considered as other qualifications: documented knowledge within human-robot interaction
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, Statistics, Bioinformatics, Applied Math, or related field. MS or PhD degree is preferred but not required. Required qualifications ? Substantial expertise in training deep learning models and tuning large
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/deep learning with GWAS/sequencing data and other types of omic data, as in Mendelian randomization and TWAS. In addition to new methods development and evaluations, the job responsibilities include
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; Machine Learning• Clinical pathways and decision support for patients with acute chest pain• AutoPiX – Explainable Deep Learning for Multimodal and Longitudinal Imaging Biomarkers in Arthritis• Speaking
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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria The position requires a PhD in machine learning, NLP, causality, or a related discipline, with a strong command of deep
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MACQUARIE UNIVERSITY - SYDNEY AUSTRALIA | North Ryde, New South Wales | Australia | about 7 hours ago
related to staff position within a Research Infrastructure? No Offer Description Salary package: Level A Step 6 (PhD Awarded Rate) $109,272 p.a., plus 17% employer's superannuation and annual leave loading
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experimental design. Proficiency with machine vision and deep learning techniques, including image segmentation, landmark placement and metric learning, for the automation of phenotypic analysis of large image