39 algorithm-development-"Multiple"-"Prof" "UNIS" Fellowship positions at Monash University
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research facility, and working in collaboration with both academic experts and the Australian Eating Disorders Research and Translation Centre , this role is critical in developing meaningful interventions
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Fellow. This role offers the chance to be at the forefront of innovation in chemical biology, with a focus on developing cutting-edge tools for molecular sensing and imaging. Under the guidance of Dr
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part in developing and evaluating neuroimaging-guided brain stimulation techniques. Situated within the vibrant Addiction and Impulsivity Research Lab , the position contributes to a global initiative
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: Developing and deploying machine learning models (e.g. graph neural networks, neural force fields, diffusion models) for molecular property prediction and molecular generation. Integrating quantum chemistry
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the University’s research capacity in a globally relevant field. The role provides the opportunity to develop and publish a monograph based on Korean Studies-related PhD research or, if already published
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Econometrics and Business Statistics at Monash University is globally recognised for its excellence in research and teaching. Our department is a leader in developing cutting-edge methodologies in econometrics
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preparation, and data analysis Assist in systematic reviews and grant development What you’ll bring: An honours degree or higher in a biomedical, allied health, or clinical research field Experience working in
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for professional development and cross-sector knowledge exchange. The ideal candidate will hold a doctoral qualification or possess equivalent research experience in a relevant field, with demonstrated expertise in
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, climate and sea level, develop key modelling technologies for ice sheet research and significantly contribute to Australian climate research. We encourage applications from candidates with strong
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with real-world application to pressing health challenges. In this role, you will: Develop new statistical methodology to support adaptive variants of cluster randomised trials Write R or Stata code