【博士奖学金】最新PhD招生和奖学金信息（208）
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The Australian plantation softwood industry produces predominantly structural timber used for construction that is more than $1.19 billion in value per annum (mill door price). UniSA has a research project funded by the National Institute for Forest Products Innovation (NIFPI) that characterises the softwood grades of this timber (based on a classification called MGP (Machine Graded Pine). The NIFPI project undertakes extensive laboratory testing to establish the physical and mechanical properties of samples collected from 14 timber mills across the country.
The aim of this project is to maximise the production of structural grade timber from the Australian plantation softwood estate by ensuring a suitable product market and grading standards exist to allow a greater proportion of smaller diameter and younger age log to be processed domestically.
With access to state-of-the-art timber equipment, opportunities to participate in timber industry events and conferences, and a chance of industry placement in a timber mill or forestry company, successful competition of this project will prepare you for a competitive career within the forestry and timber industries.
What you’ll do
Where you’ll be based
Eligibility and Selection
Essential Dates
3
Discovery of high-temperature superconductors using Deep Learning
University of Liverpool：Chemistry
Supervisor：Prof M J Rosseinsky, Dr V Gusev, Dr M Gaultois, Prof Rahul Savani
Applications accepted all year round
Funded PhD Project (Students Worldwide)
About the Project
This opportunity will remain open until the position has been filled and so early applications are encouraged.
High temperature superconductivity has great promise to transform society through the transmission of electricity with zero resistance, though the underlying physics is complex and difficult to predict from first principles, and the space of possible materials is large and equally complex. Machine learning methods have been successfully applied to many complex problems, and recent work has demonstrated such methods may also be viable to predict new functional materials with desirable properties, such as high-temperature superconductivity. In particular, deep learning methods have attracted attention for their ability to consider complex combinations of multiple attributes/features in a nonlinear fashion to predict structured outputs. This PhD project will explore the possibility of using deep convolutional neural networks to extract feature combinations and predict various properties related to superconductivity of materials. These tools will enable other key materials problems for sustainability and net zero to be tackled.
Specifically, the student will work closely with computer scientists, inorganic chemists, physicists, and material scientists to develop tools to predict new materials that may exhibit high-temperature superconductivity. This may involve developing models to identify new chemistries or regions of the periodic table where superconducting states may occur, and/or identifying new ways to improve superconducting properties (such as the transition temperature) in existing materials. As a part of this goal, the student will build models and descriptors to identify shared features in known materials that correlate strongly with the presence of high temperature superconductivity. These approaches have the potential to be expanded to the prediction of other key physical properties of importance for efficient energy use, such as ion transport in electrolytes for solid state batteries, thermoelectric materials for waste heat harvesting and magnetic and electronic materials for information storage.
The deep learning approaches applied will go far beyond the rather obsolete approaches deployed by physical computational science researchers thus far in the literature. This will be combined with the development of appropriate descriptors that use the teams understanding of materials chemistry and physics.
Applications are welcomed from students with a 2:1 or higher master’s degree or equivalent in Computer Science, Chemistry, Physics, or Materials Science, particularly those with some of the skills directly relevant to the project outlined above. Successful candidates will have strong math and programming skills. An interest and/or coursework condensed matter physics is a benefit, though not required.
Funding Notes
The stipend will be paid in line with the standard UKRI rate and tuition fees covered at the UK rate.
Discovery of solid state electrolytes using Deep Learning
University of Liverpool：Chemistry
Supervisor：Prof M J Rosseinsky, Dr V Gusev, Dr M Gaultois, Prof Rahul Savani
Applications accepted all year round
Funded PhD Project (Students Worldwide)
About the Project
In the quest towards safer and higher capacity batteries to enable electrification and a net zero society, the development of an all-solid-state battery is a top priority, and is currently limited by the lack of a high-performance material to serve as a solid state electrolyte. The interplay of many considerations including structure, bonding, and defect chemistry makes for a challenging opportunity to develop a material that is stable and is able to rapidly conduct ions in the solid state. Machine learning methods have been successfully applied to many complex problems, and recent work has demonstrated such methods may also be viable to predict new functional materials with desirable properties, such as ionic conductivity. In particular, deep learning methods have attracted attention for their ability to consider complex combinations of multiple attributes/features in a nonlinear fashion to predict structured outputs. This PhD project will explore the possibility of using deep convolutional neural networks to extract feature combinations and predict various properties related to the ionic conductivity of materials.
Specifically, the student will work closely with computer scientists, inorganic chemists, physicists, and material scientists to develop tools to predict new materials that may exhibit high ionic conductivity. This may involve developing models to identify new chemistries or regions of the periodic table where high ionic conductivity may occur, and/or identifying new ways to improve ionic conductivity in existing materials. As a part of this goal, the student will build models and descriptors to identify shared features in known materials that correlate strongly with the presence of high ionic conductivity.
The deep learning approaches applied will go far beyond the rather obsolete approaches deployed by physical computational science researchers thus far in the literature. This will be combined with the development of appropriate descriptors that use the teams understanding of materials chemistry and physics.
Applications are welcomed from students with a 2:1 or higher master’s degree or equivalent in Computer Science, Chemistry, Physics, or Materials Science, particularly those with some of the skills directly relevant to the project outlined above. Previous experience developing machine learning models is not a requirement, though successful candidates will have strong math and programming skills.
Funding Notes
The award will pay full tuition fees and a maintenance grant for 3.5 years. The maintenance grant will be at the UKRI rate, currently £15,609.00 per annum for 2021-22, subject to possible increase . The award will pay full home tuition fees and a maintenance grant for 3.5 years. Non-UK applicants may have to contribute to the higher non-UK overseas fee.
The stipend will be paid in line with the standard UKRI rate and tuition fees covered at the UK rate.
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