版块导航: 正在加载中...

登录注册

应《网络安全法》要求，自2017年10月1日起，未进行实名认证将不得使用互联网跟帖服务。为保障您的帐号能够正常使用，请尽快对帐号进行手机号验证，感谢您的理解与支持！

24小时热门版块排行榜

返回列表

【有奖交流】积极回复本帖子，参与交流，就有机会分得作者 sjnbeckham 的 75 个金币，回帖就立即获得 1 个金币，每人有 1 次机会

当前只显示满足指定条件的回帖，点击这里查看本话题的所有回帖

sjnbeckham

金虫 (初入文坛)

应助: 0 (幼儿园)
金币: 1062
帖子: 30
在线: 40.9小时
虫号: 1729851

[交流] Monash蒙纳士大学招收计算生物信息学和医学大数据分析方向博士研究生

Monash蒙纳士大学招收计算生物信息学和医学大数据分析方向博士研究生

[交流] 蒙纳士大学数据未来研究院、信息工程学部和医学部联合招收计算生物信息学和医学大数据分析方向博士研究生

Be a future thought leader - shape the future of artificial intelligence and data science for social good

蒙纳士数据未来研究院全额（提供学费、生活费及医保）博士奖学金项目
Data futures institute between the faculty of information technology and faculty of medicine, Monash university

The Monash university data futures institute aims to develop outstanding critical thinkers with a deep understanding of social, scientific and policy issues paired with training in the use of advanced artificial intelligence (ai) and data science.

Students in this program become thought leaders at the intersection of data science, ai and the human experience. the skills acquired are transferable across education, corporate, and government sectors, enabling graduates to apply their training to a broad spectrum of career paths.

Look for PhD candidates with data-driven bioinformatics/machine learning/deep learning/ image processing/computer vision background. Applicants with related first-authored papers in cs/it/se or bioinformatics & computational biology or medical imaging are preferable.

Three PhD projects are available and sponsored by data futures strategic funding for 3.5 years (with the extension of further 3 months).

Project description:
1) This PhD project will develop cutting-edge data-driven computational approaches by combining big data, generative adversarial networks, deep learning, and aim to develop cutting-edge informatic approaches for predicting the antimicrobial phenotype (AMR) to existing antibiotic regimens of bacterial pathogens from genotypic data, particularly from the whole-genomic sequence, transcriptomic and proteomic data sets. This multi-disciplinary approach will be used to better combat pathogen-causing infectious diseases. Importantly, the projects will paradigm shift the current antibiotic discovery and development in the post-antibiotic resistance era.

2) This PhD project aims to combine cutting-edge machine learning and genome-wide association analysis (GWAS) and/or transcriptome-wide association analysis (TWAS) to quantitatively assesses and characterise the relative contributions of genetic risk factors, and protective factors, in modifying risk of disease in this population. Genetic variants (DNA changes) can result in increased risk of diseases like cancer, cardiovascular disease and dementia. However, some DNA changes can be protective, and reduce risk of such diseases. Protective genetic variants play an important role in reducing disease risk, and have informed the development of drug therapies, including for cardiovascular diseases. Analysing the genomes of the heathy individuals particular from the aging population will provide a unique opportunity to discover protective genetic variants. More specifically, PhD candidates with statistical and population genetics and/or machine learning/deep learning background are encouraged to apply.

3) This PhD project aims to develop state-of-the-art computational models to identify major molecular subtypes and patient prognosis of somatic cancers from histopathological image data, and perform benchmarking studies to validate the performance of the models using various deep learning architectures. as part of the preliminary analysis, we have completed data preparation, pre-processing and code debugging for processing H&E whole-slide images (WSIs). we will address these questions using high-performance GPU supercomputers (e.g. NVidia DGX-1 and another three in-house supercomputers with 12 GPUs) based on >40,000 whole-slide images. the models will be valuable for solid tumour diagnosis and individualized precision oncology.

Major: computer science, software engineering, information technology, bioinformatics and image processing, statistical and population genetics

PhD scholarship and stipend: Three PhD scholarships will be offered, which provide a highly competitive living stipend of $40,000 per annum for up to 3.5 years and a further $3,000 in travel support (in addition to up to $16,700 one-off health insurance and relocation expenses). In addition to these, highly competitive candidates will have the opportunity to work as the part-time Research Assistant or Teaching Assistant in Prof. Jiangning Song’s Lab at the Monash Data Futures Institute and Monash Biomedicine Discovery Institute.

University ranking: Monash University is one of Australia’s group of eight - the most research-intensive universities in the country - and positioned in the top 100 universities globally
(US News world: 40th; QS World: 58th; The Times world: 57th; CWTS Leiden World: 53th). top 2 QS world university rankings of Pharmacy & Pharmacology, Top 36 QS world university rankings of medicine. We have an international reputation for interdisciplinary research with an emphasis on global issues.

Application closes soon on 31th March 2022. Competitive candidates are encouraged to contact Prof Song directly via email by sending his/her CVs and publication lists:

Jiangning.Song@monash.edu

中文简略版本:

打扰一下, 澳大利亚Monash蒙纳士大学数据未来研究院(信息工程学部和医学部)联合招收大数据驱动的生物信息和医学大数据方向博士研究生。
项目有关计算生物医学及抗生素药物大数据分析。希望招收具有计算机科学、软件工程、深度学习、生物信息学、医学图像分析等技能和知识背景的博士候选人。有计算机科学、计算视觉、生物信息学或医学图像分析等领域相关第一作者paper优先考虑。

Monash蒙纳士大学是澳大利亚八大名校之一，2021《美国新闻与世界报道》第40位；2021《QS世界大学排名》第58位；2021《泰晤士高等教育世界大学排名》中，位居世界第57位。其临床医学和生物医学学科位列世界第36位，并在药剂和药理学学科位列第2位（据2021《QS世界大学学科排名》）。

拟招收博士候选人的多项研究课题目前获得由美国国立卫生研究院（NIH）基金、澳大利亚国家健康与医疗研究理事会(NHMRC)、澳大利亚卫生部医学未来基金（MRFF）以及蒙纳士大学人工智能交叉学科重大项目等资助，研究内容涉及微生物感染全基因组测序、微生物药物耐药性、AI驱动癌症病人病理影像分析、保护性遗传变异识别算法、计算生物信息学以及生物医药大数据的数据挖掘和AI决策系统开发研究。

博士课题项目简介
1. 该博士项目将通过结合大数据、生成对抗网络、深度学习来开发前沿的数据驱动的计算方法，并旨在开发尖端信息学方法，从基因型数据预测细菌病原体现有抗生素方案的抗菌表型(AMR)，特别是从全基因组序列、转录组和蛋白质组数据集进行预测。这种多学科方法将用于更好地防治病原体引起的传染病。重要的是，这些项目将在后抗生素耐药性时代改变目前抗生素的发现和发展。多学科方法能够更好地对抗病原体引起的传染病。这些项目将在后抗生素耐药时代改变当前抗生素的发现和开发。

2. 该博士项目旨在结合先进的机器学习和全基因组关联分析(GWAS)和/或全转录组关联分析(TWAS)，定量评估和表征遗传风险因素和保护因素在改变该人群疾病风险方面的相对贡献。遗传变异（DNA变化）会导致癌症、心血管疾病和痴呆症等疾病的风险增加。然而，一些DNA变化可以起到保护作用，并降低患此类疾病的风险。保护性遗传变异在降低疾病风险方面发挥着重要作用，并促进了包括心血管疾病在内的药物疗法的发展。在没有疾病的情况下存活到高龄的健康老年人更有可能携带保护性遗传变异。分析这些有弹性的病例的基因组将为发现保护性遗传变异提供独特的机会。本项目鼓励具有统计和人口遗传学和/或机器学习/深度学习背景的学生申请。

3. 该博士项目旨在开发最先进的计算模型，以从组织病理学图像数据中识别体细胞癌的主要分子亚型和患者预后，并进行基准研究来验证各种深度学习架构验证模型的性能。作为初步分析的一部分，我们已经完成了处理 H&E 全切片图像 (WSI) 的数据准备、预处理和代码调试。我们将使用高性能 GPU 超级计算机（例如 NVidia DGX-1 和另外三台具有 12 个 GPU 的实验室内部超级计算机）基于超过 40,000 张全切片图像来解决这些问题。这些模型对于实体瘤诊断和个体化精准肿瘤学有重要意义。

欢迎有竞争力的应届、往届或者将于2021年底或2022年上半年毕业的硕士生发个人简历和publication lists咨询。

联系方式：jiangning.song@monash.edu

回复此楼

» 猜你喜欢

食品专业寻博士导师已经有7人回复
说同学对待毕业论文不认真后她很不悦已经有17人回复
2024国家自然科学基金资助额度已经有6人回复
审稿人评审完成状态还是under review 已经有11人回复
要不要二战？已经有8人回复
审稿人一收一拒，编辑给了修已经有19人回复
华南理工大学彭新文教授课题组招聘博士研究生1名（2024年9月份入学）已经有3人回复
七级岗，到手不到8000，带本科论文12教学分/生，额定教学分280 已经有24人回复
领导好安排自己做杂事。在成果上给领导挂名，请其不要经常安排自己做杂事已经有5人回复
校级优秀本科毕业论文，比例多少？看指导教师资历吗？已经有7人回复

» 本主题相关商家推荐: (我也要在这里推广)

» 抢金币啦！回帖就可以得到:
查看全部散金贴

1楼2021-12-01 17:42:57

已阅回复此楼关注TA 给TA发消息送TA红花 TA的回帖

www_啊

新虫 (初入文坛)

应助: 0 (幼儿园)
金币: 98.3
帖子: 26
在线: 9小时
虫号: 30772119

★
sjnbeckham(金币+1): 谢谢参与

码一下

发自小木虫IOS客户端