利用牛只抗逆力、采食动态、育种和终身效率提升氮素效率方向博士研究员
PhD in Better Nitrogen efficiency by improving Cow Resilience, Eating Dynamics, Breeding and Lifetime Efficiency
原帖优先：申请材料、截止时间与资格以原帖和学校官方说明为准。
AI 中文速览
- 研究内容
- 本项目将利用由CFIT 3D摄像系统生成的大量表型数据集，并结合丹麦牛数据库中的饲养参数和其他表型，开发旨在提高氮素效率、抗逆力、采食动态和饲料效率的基因工具。目标是开发用于氮素效率的新型基因组育种值，同时兼顾生产力、健康、繁殖力等其他重要育种目标性状，应用先进的统计方法、人工智能和前沿遗传模型来支持奶牛场的管理和育种决策，为农业绿色转型做出贡献。
- 申请条件
- 申请者需持有动物育种相关专业硕士学位（120 ECTS）；具备数据处理软件（如 R、SAS、Python、Unix Shell）和遗传软件（如 DMU、BLUPF90、AIREML）的使用经验；有处理家畜数据背景者为优势；流利的英语听说读写能力是必修前提。
- 待遇
- 原文未说明薪资待遇与合同具体细节。
- 申请方式
- 申请者需通过网页上的“Apply”按钮提交申请。截止时间为2026年11月29日23:59 CET，首选开学日期为2027年3月1日。
- 材料清单
- 项目描述（需复制上文项目描述并作为 PDF 上传）
由 gemini-2.5-flash-lite 生成，博士岗判定置信度 100%。
岗位信息
- 截止
- （Europe/Copenhagen） 剩 31 天
- 学科
- 农业与生物科学
- 合同类型
- 雇佣合同
- 原帖发布
- 本站收录
- 内容更新
- 原帖导师候选（待核验）
- Bingjie Li, Rasmus Bak Stephansen
- 来源
- jobs.ac.uk（英国博士项目及学术招聘） · 最近核对 2026-10-28
- 详情核验
判定依据（原文摘录）
- is_phd
Applicants are invited for a PhD Fellowship/Scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Quantitative Genetics and Genomics programme.
- english_ok
The ability to speak and write English fluently is a prerequisite for the applicant.
- bachelor_ok
A relevant master’s degree (120 ECTS) in Animal Breeding
原文
Applicants are invited for a PhD Fellowship/Scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Quantitative Genetics and Genomics programme. The position is available from 1 March 2027 or later. You can submit your application via the link under 'how to apply'.
Title:
Better Nitrogen efficiency by improving Cow Resilience, Eating Dynamics, Breeding and Lifetime Efficiency
Research area and project description:
The Center for Quantitative Genetics and Genomics (QGG) holds a leading position in cattle breeding research, particularly in genetic studies on data generated by advanced phenotyping technologies such as the CFIT 3D camera system through industry collaboration with VikingGenetics. This state-of-the-art system enables large-scale, non-invasive measurement of feed intake, body weight, and milk production data in over 30,000 cows directly in their production environment. This PhD project will leverage this extensive phenotypic dataset generated by CFIT, in combination with feeding parameters and other phenotypes from the Danish Cattle Database, to develop genetic tools aimed at enhancing nitrogen efficiency, resilience, eating dynamics and feed efficiency. The goal is to develop novel genomic breeding values for nitrogen efficiency, while considering other important breeding goal traits (e.g., productivity, health, fertility) and other traits, such as resilience, eating dynamics and feed efficiency. The project will apply advanced statistical methods, artificial intelligence, and cutting-edge genetic models to support and enhance management and breeding decision-making by dairy farmers. This project offers a unique opportunity to contribute to the green transition in agriculture by breeding cows that are both more sustainable and economically viable for the future of Danish dairy farming. We encourage candidates finishing their MSc in summer 2027 also to apply.
Qualifications and specific competences:
• A relevant master’s degree (120 ECTS) in Animal Breeding
• Experience with data handling softwares (e.g., R, SAS, Python, Unix Shell) and genetic softwares (e.g., DMU, BLUPF90, AIREML).
• Background in working with Livestock data is an advantage.
• The ability to speak and write English fluently is a prerequisite for the applicant.
Place of employment and place of work:
The place of employment is Aarhus University, and the place of work is Center for Quantitative Genetics and Genomics, C.F. Møllers Allé 3, 8000 Aarhus C., Denmark.
Contacts:
Applicants seeking further information regarding the PhD position are invited to contact:
• Associate Professor Bingjie Li, e-mail: bingjie.li@qgg.au.dk (main supervisor)
• Tenure Track Assistant Professor Rasmus Bak Stephansen, e-mail: rasmus.stephansen@qgg.au.dk (co-supervisor)
For information about application requirements and mandatory attachments, please see our application guide . If answers cannot be found there, please contact:
• admission.gradschool.tech@au.dk
How to apply:
To submit your application, click the 'Apply' button
Application deadline is 29 November 2026 23:59 CET.
Preferred starting date is 01 March 2027.
For technical reasons, you must upload a project description. Please copy the project description above and upload it as a PDF in the application.
Please note:
• Only documents received prior to the application deadline will be evaluated. Thus, documents sent after deadline will not be considered.
• The programme committee may request further information or invite the applicant to attend an interview.
• Shortlisting will be used, which means that the evaluation committee only will evaluate the most relevant applications.