学习分析、社会学习与连通性方向博士职位
Research Associate / Doctoral Candidate (m/f/d) Learning Analytics for Social Learning and Connectedness
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
- 博士研究人员将利用数字通信数据开发计算指标，捕捉互动和连接模式，建模学习关系的发展，并构建支持学习者之间连通性的干预措施；职位还包括教育技术和学习分析领域的少量教学工作。
- 申请条件
- 申请人须持有社会心理学、计算语言学、计算社会科学、语言学、传播学或相关强定量背景专业的硕士学位（或同等学历），具备定量研究与数据分析经验，熟练掌握 R 或 Python 等统计/编程工具，并具备优秀的英语书面和口头表达能力。
- 待遇
- 提供 TV-L E13（50%）标准的薪酬，岗位初始期限为3年，提供优秀的指导和学术监督、TUM研究生院的博士生培养、灵活的工作安排以及慕尼黑工业大学和慕尼黑数据科学研究所的优良研究基础设施。
- 申请方式
- 请将完整的申请材料（求职信、简历、成绩单、硕士论文或相关出版物、推荐人联系方式）打包为一个 PDF 发送至指定邮箱 office.lea@sot.tum.de，滚动录取，截止日期为2026年3月25日。
- 材料清单
- 求职信 (motivation letter)
- 简历 (CV)
- 成绩单 (transcripts)
- 硕士论文或相关出版物 (Master’s thesis or relevant publications)
- 推荐人联系方式 (contact details of references)
由 gemini-2.5-flash-lite 生成，博士岗判定置信度 100%。
结构化信息
- 截止
- 原帖未给出
- 学科
- 社会科学
- 合同类型
- 雇佣合同
- 本站收录
- 内容更新
- 导师
- Prof. Dr. Oleksandra Poquet
- 来源
- Technische Universität München 官方招聘 · 最近核对 2026-10-07
判定依据（原文摘录）
- is_phd
Research Associate / Doctoral Candidate (m/f/d) Learning Analytics for Social Learning and Connectedness
- english_ok
Excellent written and spoken English; German language skills are an advantage
- bachelor_ok
Completed Master’s degree (or equivalent) in social psychology, computational linguistics, computational social science, linguistics, communication science, or a related field with a strong quantitative profile
原文
Research Associate / Doctoral Candidate (m/f/d) Learning Analytics for Social Learning and Connectedness
04.03.2026, Academic staff
The Professorship for Learning Analytics (LEAPS) at the TUM School of Social Sciences and Technology, Technical University of Munich, is seeking a
Research Associate / Doctoral Candidate (m/f/d) Learning Analytics for Social Learning and Connectedness
The position is TV-L E13, 50%, initially limited to 3 years. Applications are reviewed on a rolling basis (first come, first served). Application deadline: 25 March 2026.
About Us
The candidate will be a part of the LEAPS research group (LEarning Analytics and Practices in Systems) led by Prof. Dr. Oleksandra Poquet. LEAPS investigates how data from learning environments can support agency and social networks in higher education and workplace training. The group is part of the TUM School of Social Sciences and Technology, the Munich Data Science Institute, and the TUM EdTech Centre.
Project Description
When students communicate online through forums, collaborative documents, or chat, they leave traces of how they interact and connect with each other. The doctoral researcher will develop computational indicators that capture these patterns from digital communication data, model how learning relationships form and evolve, and use these insights to build interventions that support connectedness among learners. The position includes some teaching in the areas of educational technology and learning analytics.
Your Profile
• Completed Master’s degree (or equivalent) in social psychology, computational linguistics, computational social science, linguistics, communication science, or a related field with a strong quantitative profile • Experience with quantitative research methods and data analysis • Knowledge of network science methods, natural language processing, or computational text analysis is an advantage • Proficiency in statistical or programming tools (e.g., R, Python) • Interest in education and learning as an application domain • Ability to work independently • Demonstrated academic writing ability (e.g., Master’s thesis, publications, or conference contributions) • Excellent written and spoken English; German language skills are an advantage • Ability to work in an interdisciplinary team
What We Offer
• A research environment that rewards intellectual courage and hard work, gives you the freedom and support to pursue ideas that challenge the status quo, and where you will learn a great deal. • Excellent mentorship and academic supervision • Strong international and local network • Doctoral training through the TUM Graduate School • Active involvement in academic communities (e.g., SoLAR, EATEL) • Flexible working arrangements • Access to the excellent research infrastructure of TUM and the Munich Data Science Institute • Remuneration according to TV-L E13 (50%)
Please send your complete application (motivation letter, CV, transcripts, Master’s thesis or relevant publications, contact details of references) as a PDF to: office.lea@sot.tum.de
TUM is an equal opportunity employer committed to increasing the proportion of women in its workforce. Applications from women are therefore expressly encouraged. Candidates with disabilities who are otherwise equally qualified will be given preference.
The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance.
Data Protection Information:
When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.
Kontakt: office.lea@sot.tum.de
More Information
https://tumapply.aet.cit.tum.de/job/detail/cef51faf-834d-4dc0-ac2e-31ef8a33f72b