MCOG Research Fellowship is offered to medical students, interns, and new graduates to build up their basic research knowledge and improve their practical skills. Candidates are selected through an interview process, and they are offered practical teaching through direct implementation of a series of theoretical lectures in actual research projects. Fellows gain experience in different fields of research by attending rotations in a variety of MCOG units and offices to ensure broad exposure to scientific research. Top-rated fellows are offered higher level leadership positions in the research and innovations office if they are interested. The duration of the fellowship is 1 year and is completely fees-free.
 

Program Objectives:

• Introduce basic research concepts to fellows.

• Provide the opportunity to apply research concepts to actual research projects.

• Explain evidence-based medicine and involve fellows in creation of evidence and development of clinical guidelines.

• Learn basic principles of machine learning

• Learn basic principles of computer programming

• Be offered the opportunity to acquire teaching and educational skills

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Eligibility:

All medical students, interns and young trainees are welcome to apply for the fellowship. Previous research experience is not mandatory. Priority is given to candidates who are able to show high personal values and commitment to research and assisting younger generations in the future.

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Program structure:

• Preparation (Bootcamp): It is the condensed learning part where all the fellows must finish the learning resources as a step of basic knowledge in order to join the projects.

• Two main projects:

First one is a systematic review, which will be held by MCOG research and innovations office.

Second one is a guideline, which will be held by MCOG practice and education office.

• Rotations: In addition to the main projects, fellows will be asked to do additional rotations in other areas of the foundation and be involved in their projects (e.g., multicenter studies, clinical trials, machine learning and statistics).

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