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- Tasks: Collaborate on long-term, data-driven research across various disciplines.
- Company: Innovative research team focused on impactful, interdisciplinary projects.
- Benefits: Flexible collaboration, opportunity to shape research directions, and access to large-scale datasets.
- Why this job: Join a dynamic team and make a real difference in data-centric research.
- Qualifications: Experience in managing large datasets and conducting multi-disciplinary research.
- Other info: Ideal for those seeking meaningful, long-term research partnerships.
The predicted salary is between 36000 - 60000 £ per year.
We collaborate long-term with teams engaged in large-scale, data-driven research. Our research domains include, but are not limited to:
- Healthcare and real-world data (RWD)
- Engineering and applied modeling
- Computer science and artificial intelligence
- Social sciences and public / population health
Our work is data-centric and research-oriented. We do not operate on a task-based or single-paper writing model.
We are interested in connecting with research leads or small research teams that already operate within a structured research framework. You or your team may be a good fit if you:
- Manage or curate reusable, large-scale datasets (medical, engineering, computational, or multi-source data)
- Maintain topic pipelines or long-term research agendas, rather than ad-hoc writing
- Conduct multi-disciplinary research based on shared data or methodological frameworks
- Have published across different disciplines or journal categories
This is not a traditional employment role and not an outsourcing arrangement. We are looking for long-term, research-level collaboration, where:
- Research directions are initiated based on data and methodology
- Publication planning is structured and scalable
- Writing begins only after research directions are clearly defined
Relevant Background (Not Mandatory):
- Long-term involvement in data-driven research (healthcare, engineering, computer science, or interdisciplinary)
- Experience with large-scale data analysis, statistical modeling, or machine learning
- Experience leading or coordinating research teams
- Ability to think in terms of research systems, rather than single manuscripts
We value alignment in research philosophy more than formal titles or resumes. If your work is data-centric, system-oriented, and focused on long-term research collaboration, we would be glad to start a conversation.
Interdisciplinary Data-Driven Research Partner employer: Fengkai Group Co., Limited
Contact Detail:
Fengkai Group Co., Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Interdisciplinary Data-Driven Research Partner
✨Tip Number 1
Network like a pro! Reach out to your connections in the research community and let them know you're on the lookout for opportunities. Attend conferences or webinars related to data-driven research to meet potential collaborators and showcase your expertise.
✨Tip Number 2
Showcase your work! Create a portfolio that highlights your previous research projects, datasets you've managed, and any publications. This will give potential collaborators a clear idea of what you bring to the table and how you can contribute to long-term research.
✨Tip Number 3
Be proactive! Don’t wait for job postings to appear. Reach out directly to teams or organisations that align with your research interests. A well-crafted email expressing your interest in collaboration can open doors to exciting opportunities.
✨Tip Number 4
Apply through our website! We’re always on the lookout for passionate researchers who fit our collaborative model. By applying directly, you’ll ensure your application gets the attention it deserves and you’ll be one step closer to joining our innovative team.
We think you need these skills to ace Interdisciplinary Data-Driven Research Partner
Some tips for your application 🫡
Show Your Research Passion: When writing your application, let your enthusiasm for data-driven research shine through. Share specific examples of your past projects or collaborations that highlight your commitment to long-term research and how they align with our values at StudySmarter.
Be Clear and Concise: We appreciate clarity! Make sure your application is well-structured and easy to read. Avoid jargon unless it's necessary, and focus on conveying your experience and skills in a straightforward manner that reflects your understanding of interdisciplinary research.
Highlight Relevant Experience: Tailor your application to showcase your experience with large-scale datasets and multi-disciplinary research. Mention any specific methodologies or frameworks you've worked with that could be relevant to our collaborative approach at StudySmarter.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Fengkai Group Co., Limited
✨Know Your Data Inside Out
Make sure you’re well-versed in the datasets you’ve worked with. Be ready to discuss how you managed or curated large-scale datasets, and share specific examples of your experience in data-driven research.
✨Showcase Your Research Framework
Prepare to explain your structured research framework. Highlight how you maintain long-term research agendas and topic pipelines, rather than just focusing on ad-hoc writing. This will demonstrate your alignment with their collaborative approach.
✨Emphasise Interdisciplinary Collaboration
Since they value multi-disciplinary research, be prepared to discuss your experiences working across different fields. Share examples of how you’ve collaborated with teams from various disciplines and how that has enriched your research.
✨Think Long-Term
During the interview, focus on your vision for long-term research collaboration. Discuss how you plan to initiate research directions based on data and methodology, and how you see your role evolving within a collaborative framework.
