中国科学院城市环境研究所流域水环境研究组博士后招聘启事
发布时间:2022-04-24
一、岗位要求
对流域水系统生境复杂性、污染物分异性和生物多样性及其尺度关联性的数据挖掘具有浓厚研究兴趣,获得相关专业博士学位一般不超过3年,年龄不超过35周岁。申请精英/技术英才博士后/特别研究助理,成果需符合以下条件之一:
1. 以第一作者至少发表3篇高水平SCI论文;
2. 如有第一申请人(导师第一且本人第二的视同第一)的授权专利1项,则至少发表2篇高水平SCI论文;
3. 经招聘委员会认定为高水平论文,可不限论文数量;
4. 在数据挖掘和可视化等相关软件研发和运用方面工作突出,并有相关证明材料,经招聘委员会认定符合特别研究助理要求,且经所务会审核通过。
二、岗位待遇
1. 精英/技术英才博士后/特别研究助理年薪参照副高平均水平,一般不低于24万/年,普通全职博士后参照副高平均水平的70%;精英/技术英才/普通全职博士后均可享受宁波市和北仑区最高55万元生活补助。
2. 对于创新潜质和科研业绩突出的博士后,将有额外的津贴。
3. 当年入职的博士后可申请当年度“中国科学院特别研究助理资助项目”。项目获得者可获中科院60万元资助,分2年下达。项目执行期内可再享受项目津贴。
三、岗位职责
1. 热爱研究工作,负责水系统(江河水-湖库水-湿地水-地下水-农田水-饮用水-管网水-再生水-排口水-河口水-海湾水)生境特征、污染物浓度和生物群落等野外监测、样品测试及信息和数据流分析等科研工作,积极申请各类相关科研项目。
2. 责任心和服务意识强,既能独立工作又能合作攻关,主动融入研究所、观测站、研究组等科研氛围和学习习惯,积极调研地方管理部门和环境治理企业的科技需求并承接相关委托项目。
四、应聘材料
1. 《中国科学院城市环境研究所应聘登记表》,下载链接:http://www.iue.cas.cn/ztbd/xzzq/rsjy/
2. 个人资料(个人简历、学历和学位证书、获奖证书、博士学位论文简介等);
3. 两位推荐人的推荐信,其中一位推荐人必须是其博士阶段导师。推荐信必须是具有推荐人亲笔签名的扫描件。
申请人将应聘材料发送至相应研究组负责人邮箱,同时抄送人力资源处邮箱(zhaopin@iue.ac.cn),邮件主题命名为:姓名-应聘团队岗位。例如,张三-应聘流域水环境研究组xx岗位。
五、联系方式
1. 研究组负责人:徐耀阳 研究员;邮箱:yyxu@iue.ac.cn
2. 人力资源处联系人:阙老师;邮箱:zhaopin@iue.ac.cn,电话:0592-6190966
3. 联系地址:宁波市北仑区春晓中科路88号(邮编315800)
六、成果参考
1. Liu D., Xu Y., Junaid M., Zhu Y., Wang J., 2022. Distribution, transfer, ecological and human health risks of antibiotics in bay ecosystems. Environment International, 158: 106949.
2. Guo Z., Boeing W.J., Xu Y., Borgomeo E., Mason S.A., Zhu Y., 2021. Global meta-analysis of microplastic contamination in reservoirs with a novel framework. Water Research, 207: 117828.
3. Guo Z., Boeing W.J., Xu Y., Yan C., Faghihinia M., Liu D., 2021. Revisiting seasonal dynamics of total nitrogen in reservoirs with a systematic framework for mining data from existing publications. Water Research, 201: 117380.
4. Liang Z., Liu Y., Xu Y., Wagner T., 2021. Bayesian change point quantile regression approach to enhance the understanding of shifting phytoplankton-dimethyl sulfide relationships in aquatic ecosystems. Water Research, 201: 117287.
5. He Y., Li G., Zhou S., Li R., Zhao J., Wu K., Wang J., Jia X., Wu X., Gao F., Xu Y., Bao P., 2021. Bacteria involved in thiosulfate reduction coupled with anaerobic ammonium oxidation in the critical zone groundwater. ACS Earth and Space Chemistry, 5(8), 2142-2151.
6. Li G. & Bao P., 2021. Transcriptomics analysis of the metabolic mechanisms of iron reduction induced by sulfate reduction mediated by sulfate-reducing bacteria. FEMS Microbiology Ecology, 97, fiab005.
7. Tang J, Sun J, Wang W, Yang L, Xu Y., 2021. Pharmaceuticals in two watersheds in Eastern China and their ecological risks. Environmental Pollution, 116773.
8. Qiu Q., Liang Z., Xu Y., Shin-ichiro S.M., Komatsu K., Wagner T., 2021. A statistical framework to track temporal dependence of chlorophyll–nutrient relationships with implications for lake eutrophication management. Journal of Hydrology, 603: 127134.
9. Guo Z., Boeing W.J., Borgomeo E., Xu Y., Weng Y., 2021. Linking reservoir ecosystems research to the Sustainable Development Goals. Science of the Total Environment, 781: 146769.
10. Tang J, Wang W, Yang L., 2020. Seasonal variation and ecological risk assessment of dissolved organic matter in a peri-urban critical zone observatory watershed. Science of The Total Environment, 707: 136093.
11. Tang J, Li X, Cao C, Lin M, Qiu Q, Xu Y., 2019. Compositional variety of dissolved organic matter and its correlation with water quality in peri-urban and urban river watersheds. Ecological Indicators, 104: 459–469.
12. Xu Y., Schroth A.W., Isles P.D.F., Rizzo D.M., 2015. Quantile regression improves models of lake eutrophication with implications for ecosystem-specific management. Freshwater Biology, 60(9): 1841-1853.
13. Xu Y., Schroth A.W., Rizzo D.M., 2015. Developing a 21st Century framework for lake-specific eutrophication assessment using quantile regression. Limnology and Oceanography: Method, 13(5): 237-249.
14. Xu Y., Boeing W.J., 2014. Modeling maximum lipid productivity of microalgae: Review and next step. Renewable and Sustainable Energy Reviews, 32: 29-39.
15. Xu Y., Boeing W.J., 2013. Mapping biofuel field: A bibliometric evaluation of research output. Renewable and Sustainable Energy Reviews, 28: 82-91.
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