An innovative research program focused on the diagnosis and treatment of major respiratory diseases, with a particular emphasis on post-operative evaluation of minimal residual disease (MRD) in lung cancer. This program utilizes artificial intelligence (AI) and multi-omics data to develop an advanced evaluation system for post-surgical MRD in lung cancer patients. Additionally, the program aims to establish and apply a predictive model for post-operative recurrence, integrating cutting-edge technologies to enhance early detection, personalized treatment strategies, and outcomes prediction.

· Focus on AI-driven multi-omics analysis for lung cancer

· Integration of artificial intelligence and clinical applications

· Development of advanced post-surgical minimal residual disease (MRD) evaluation system

· Creation of predictive models for post-operative recurrence

· Access to cutting-edge research tools and facilities

· Hands-on experience in AI-based data analysis and clinical research

· Collaboration with clinical teams for real-world applications

· Direct mentorship from experts in AI, oncology, and clinical research

· Master fundamental concepts of multi-omics and AI applications in lung cancer
· Develop expertise in post-operative MRD detection and recurrence prediction models
· Learn advanced data analysis techniques using AI and multi-omics approaches

· Understand clinical trial design for recurrence prediction in lung cancer
· Gain experience in translational research linking AI models to clinical outcomes

· Develop scientific writing and presentation skills for research publications and conferences

8 weeks

Key Research Areas:

1. Development of AI-based multi-omics systems for post-operative MRD evaluation in lung cancer

2. Creation of predictive models for lung cancer recurrence after surgery

3. Application of artificial intelligence in personalized treatment strategies for lung cancer patients

Current Projects:

1. AI-driven multi-omics analysis for post-surgical minimal residual disease detection in lung cancer

2. Development of predictive models for recurrence in lung cancer patients post-surgery

3. Integration of AI and clinical data for enhancing treatment outcomes in lung cancer

Undergraduate students:

Candidates should be enrolled in a bachelor's degree program in biology, biochemistry, or a related field. Prior coursework in molecular biology or immunology is preferred. Basic proficiency in English is required.

Graduate students:

Candidates must be enrolled in a Master’s, M.D. or Ph.D. program in biomedicine, immunology, or a related field. Familiarity with research methodologies and laboratory techniques is required. Fluency in English is mandatory.

Researcher fellows:

Applicants should have an advanced degree (M.Sc. M.D. Ph.D. or equivalent) in a biomedical discipline with some prior research experience. Candidates must demonstrate an ability to work independently and contribute to team projects. Proficiency in English is essential.

Doctors:

Candidates must hold an M.D. degree with clinical experience, preferably in oncology or internal medicine. Research experience is not mandatory but is a strong advantage. Applicants must be fluent in English.

Postdocs:

Postdoctoral fellows should have a Ph.D. in biomedicine, immunology, or an M.D. with clinical training in medicine, or the equivalent of education, training and experience. Candidates should be fluent in English.

Daily Training Schedule (Example):

Monday:

· 9:00-10:30 Laboratory meeting

· 11:00-12:00 Technical training

· 14:00-17:00 Experimental work

Tuesday:

· 9:00-12:00 Research experiments

· 14:00-15:30 Journal club

· 16:00-17:00 Data analysis

Wednesday:

· 9:00-12:00 Clinical correlation meeting

· 14:00-17:00 Laboratory work

Thursday:

· 9:00-12:00 Research experiments

· 14:00-15:30 Scientific writing workshop

· 16:00-17:00 Individual mentoring

Friday:

· 9:00-10:30 Progress presentation

· 11:00-12:00 Group discussion

14:00-17:00 Laboratory work

· Monthly progress reports

· Research presentation evaluations

· Laboratory technique assessments

· Final project presentation

· Publication contribution

· Certificate upon successful completion

· Regular feedback sessions with mentor

· End-of-program evaluation survey

The program is free of charge.