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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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Instructions To be considered, candidates should apply online at UF Careers website and attach the following materials: A research statement outlining the focus and future vision of your research program in one
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, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If you are unable to use our online application process due to an impairment or disability
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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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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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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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the areas of experimental and theoretical physics, synthetic, physical and computational chemistry, material sciences and related areas. The Opportunity The OPTEXC IRTG involves 20 academics in Australia and
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the development of both, the quantum internet and distributed quantum computing. The objectives of this PhD thesis project are: (a) Demonstrate spin-photon entanglement with single colour centres in silicon carbide
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qualifications As our new colleague in our research team your job will be to develop novel computational frameworks for machine learning. In particular, you will push the boundaries of Scalability, drawing upon
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short courses in the core subjects of this PhD programme including process intensification and green chemistry. This project is part of the Process Industries: Net Zero (PINZ) Centre for Doctoral training