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About Us
We maintain long-term, in-depth collaborations with research teams engaged in large-scale, data-driven research.
Our research areas are broad, covering but not limited to:
– Composite materials
– Engineering and applied modeling
– Computer science and artificial intelligence
– Social sciences and public / population health
We are data-centric and research-driven, and we do not adopt a task-based or single-manuscript writing cooperation model.
Who We Are Looking For
We are looking to connect with research leads or small research teams that already operate within a structured research framework. You or your team will be ideal partners if you have deep expertise in any of the following fields:
- Materials Science: composite materials, biomaterials, coatings and thin films
- Engineering: telecommunications engineering, environmental engineering, mechanics, automation and control systems, civil engineering, optics, etc.
- Chemistry: physical chemistry, nanoscience and nanotechnology, energy and fuels, medicinal chemistry
- Agricultural Science: agronomy, agriculture, dairy and animal science
- Mathematics: applied mathematics
- Earth Science: earth science (multidisciplinary), meteorology and atmospheric sciences, oceanography, physical geography
We hope you focus on more than just individual papers, and possess the following qualities:
– Maintain clear topic pipelines or long-term research agendas, rather than ad-hoc writing
– Skilled in conducting interdisciplinary research based on shared data or methodological frameworks
– Have publication experience across different disciplines or types of journals
How Collaboration Works (Important)
This is not a traditional employment relationship, nor a simple outsourcing arrangement.
We are looking for long-term, in-depth, research-level collaboration, as follows:
– Research directions are initiated jointly based on data and methodology
– Publication planning is structured and scalable
– Manuscript writing begins only after a clear consensus on research direction is reached
Relevant Background (Not Mandatory)
The following backgrounds will be considered a plus, but are not required:
– Long-term involvement in data-driven research (engineering, materials science, computer science, or interdisciplinary fields)
– Experience in large-scale data analysis, statistical modeling, or machine learning
– Experience leading or coordinating research teams
– Ability to think from the perspective of a research system, rather than focusing solely on individual papers
Our Philosophy
We value alignment in research philosophy more than formal titles and resumes.
If your work is data-centric and system-oriented, and you are focused on long-term, stable research collaboration, we would be very glad to start a conversation.
