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澳大利亞國(guó)立大學(xué)(全球30名)全獎(jiǎng):使用具身智能及機(jī)器學(xué)習(xí)開(kāi)發(fā)下一代光伏材料【急】
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項(xiàng)目名稱(chēng): 使用具身智能及機(jī)器學(xué)習(xí)開(kāi)發(fā)下一代光伏材料(請(qǐng)聯(lián)系hualin.zhan@anu.edu.au申請(qǐng)) 院校: 澳大利亞國(guó)立大學(xué)計(jì)算機(jī)及工程學(xué)院 入學(xué)時(shí)間: 2025年3月31日前(獲得全獎(jiǎng)無(wú)額外要求)或2025年8月31日后(獲得全獎(jiǎng)有額外要求) 學(xué)生專(zhuān)業(yè)要求:計(jì)算機(jī)(全年接收申請(qǐng)) 數(shù)學(xué)、物理、半導(dǎo)體(接收8月31日后入學(xué)申請(qǐng)) 獎(jiǎng)學(xué)金信息(全獎(jiǎng)): 生活費(fèi):每年 AUD$38,154 (人民幣約17萬(wàn)9千) 學(xué)費(fèi):免費(fèi)(必須達(dá)到以下第1點(diǎn)硬性要求) 獎(jiǎng)學(xué)金要求: 1. 硬性要求(適用于所有申請(qǐng)者): GPA > 3.2/4 畢業(yè)院校:211或985或雙一流(一般本科不低于211,達(dá)到額外要求者可特殊考慮) 2. 額外要求(適用于8月31日后入學(xué)): 達(dá)到以下要求之一即可。申請(qǐng)者須為一作或共一 2.1 發(fā)表本領(lǐng)域內(nèi)至少1篇高影響力論文(影響因子>20) 2.2 至少1篇高影響力會(huì)議論文(見(jiàn)附表) 2.3 至少3篇高質(zhì)量論文(影響因子>5) 2.4 至少3份專(zhuān)利 項(xiàng)目導(dǎo)師: Dr Hualin Zhan(本項(xiàng)目主導(dǎo)師,澳大利亞先進(jìn)光伏中心 ACAP Fellow) 郵件: hualin.zhan@anu.edu.au Prof Kylie Catchpole(本項(xiàng)目博士生Chair,澳大利亞科學(xué)院及工程院雙院士) 注意:該項(xiàng)目需要經(jīng)常往返堪培拉(澳大利亞國(guó)立大學(xué))及墨爾本(莫納什大學(xué))。旅行經(jīng)費(fèi)由項(xiàng)目提供。 項(xiàng)目介紹: We are a world-leading group in the field of photovoltaics. Our world-record perovskite solar cells have identified great opportunities and key challenges to revolutionize the next-generation photovoltaics (publications in nature, science, energy environ. Sci., etc.). This project aims to address these key challenges, in which we will use machine learning to deliver a step-change in the field’s capacity to precisely control the stability of perovskite and to rationally design the perovskite-based solar cells. We have already developed a prototype machine learning platform that is ideal for future PhD studies on this project (recently published in Energy Environ. Sci.). Successful candidates will work in a friendly environment with access to world-class photovoltaics fabrication, characterization, and computation facilities, such as the Australian Centre for Advanced Photovoltaics, Australian National Fabrication Facility, National Computational Infrastructure, etc. The act nodes of these facilities are all hosted by the Australian National University. Candidates will have opportunities to visit our close collaborators in Germany, United States, China, and other states in Australia. Candidates are expected to attend domestic and international conferences. Project-funded students must visit Monash University regularly (travel costs will be covered). Candidates with a keen interest in technological revolution of perovskite photovoltaics using embodied AI and machine learning are encouraged to apply. Experience in machine learning, mathematics, physics, and/or perovskite materials engineering is highly preferred. If you are passionate about engineering science and impactful research, we invite you to apply for this exciting opportunity to shape the future of sustainable energy technologies. Selected publications: H. Zhan, et. al., Energy Environ. Sci., 2024, 17, 4735-4745. J. Peng, et. al., Nature, 2022, 601, 573-578. J. Peng, et. al., Science, 2021, 371, 390. H. Shen, et. al., Energy Environ. Sci., 2018, 11, 394-406. 附表:高影響力會(huì)議 Publication h5-index h5-median 1. Neural Information Processing Systems 337 614 2. International Conference On Learning Representations 304 584 3. International Conference On Machine Learning 268 424 4. AAAI Conference On Artificial Intelligence 220 341 5. Expert Systems With Applications 165 228 6. IEEE Transactions On Systems, Man And Cybernetics Part B, Cybernetics 155 212 7. IEEE Transactions On Neural Networks And Learning Systems 149 215 8. Neurocomputing 136 210 9. International Joint Conference On Artificial Intelligence (Ijcai) 136 192 10. Neural Computing And Applications 135 184 11. Information Fusion 134 214 12. Applied Soft Computing 133 171 13. Knowledge-Based Systems 126 198 14. Artificial Intelligence Review 118 198 15. Journal Of Machine Learning Research 117 202 16. IEEE Transactions On Fuzzy Systems 111 160 17. International Conference On Artificial Intelligence And Statistics 100 162 18. Engineering Applications Of Artificial Intelligence 97 135 19. Neural Networks 95 146 20. Conference On Robot Learning 88 150 |
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