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://www.latrobe.edu.au/study/scholarships/other/connecting-spatial-and-spectral-information-understanding-complex-materials-systems-at-the-molecular-level-with-machine-learning-phd-scholarship Who to contact for further
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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resource-efficiency requirements. This collaborative doctoral project brings together the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy
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profound knowledge in computational and theoretical physics/chemistry. Capability of team work is essential. Skills in high-performance computing, materials chemistry, theoretical chemistry, molecular
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that demand interdisciplinary solutions? Then the Program for Collaborative Doctoral Projects is the perfect opportunity for you. Many of today’s most pressing problems can only be tackled through
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Supervisors: Professor Richard Hague1 , Professor Chris Tuck1 , Dr Geoffrey Rivers1 (1 Faculty of Engineering) PhD project description: Inkjet printing allows multiple materials to be 3D-printed
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development and marine management. Your primary tasks will be to: Compile and harmonize data from multiple sources (e.g., EMODnet, Copernicus, fisheries surveys, citizen science). Engage with data managers and
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ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. Approval and Enrolment The scholarship for the PhD degree is subject to academic approval, and the
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of Science and Technology (NTNU) offers a joint 3-year PhD fellowship. Novel non-target chemical analyses have recently revealed that groundwater and drinking water are contaminated from PFAS, pesticide and