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PH-D in Computational Biology Cb at Indraprastha Institute of Information Technology Delhi
Delhi, Delhi
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About the Specialization
What is Computational Biology (CB) at Indraprastha Institute of Information Technology Delhi Delhi?
This Computational Biology (CB) Ph.D. program at IIIT-Delhi focuses on interdisciplinary research at the intersection of computer science, biology, and medicine. It leverages advanced computational techniques like AI, machine learning, and data science to address complex biological problems, driving innovation in areas like genomics, proteomics, systems biology, and drug discovery, crucial for India''''s evolving biotechnology sector.
Who Should Apply?
This program is ideal for highly motivated individuals holding B.Tech/M.Tech/M.Sc. degrees in computer science, bioinformatics, biotechnology, or related fields. It targets fresh graduates passionate about cutting-edge research and working professionals seeking to transition into or deepen their expertise in data-driven biological research, contributing to India''''s burgeoning biotech and healthcare industries.
Why Choose This Course?
Graduates of this program can expect to pursue high-impact research careers in academia, R&D labs, and leading biotech/pharma companies in India and globally. Career paths include computational biologist, data scientist (biomedical), bioinformatics specialist, and AI/ML researcher. Entry-level salaries in India typically range from INR 8-15 LPA, with experienced professionals earning INR 20-50+ LPA in top organizations, aligning with India''''s growing focus on health-tech.
Student Success Practices
Foundation Stage
Master Research Fundamentals and Core Coursework- (Year 1)
Dedicate initial semesters to rigorously understanding core subjects like Research Methodology, Communication Skills, and advanced CB/CS/Bio courses. Actively participate in class discussions, complete assignments diligently, and seek regular feedback from professors to build a strong theoretical foundation.
Tools & Resources
IIIT-Delhi Course materials, Academic journals (e.g., Nature, Science, Cell), Online platforms like Coursera/edX for foundational refreshers, Peer study groups
Career Connection
A solid foundation is critical for developing research questions, designing experiments, and effectively communicating findings, directly impacting thesis quality and future research career opportunities.
Proactively Engage with Research Labs- (Year 1)
Identify potential Ph.D. supervisors and their labs early on. Attend departmental seminars, research group meetings, and interact with senior Ph.D. students and faculty. Aim to join a lab as soon as possible, even in a supporting role, to gain practical research exposure and understand the lab''''s research focus.
Tools & Resources
IIIT-Delhi Department websites, Faculty research profiles, Ph.D. student presentations, Research colloquia
Career Connection
Early lab engagement helps in choosing the right research area and supervisor, builds practical skills, and is crucial for developing a compelling Ph.D. proposal and subsequent thesis, essential for academic and industry R&D roles.
Cultivate Strong Programming and Data Science Skills- (Year 1-2)
Computational Biology heavily relies on strong programming (Python, R) and data handling skills. Consistently practice coding, participate in hackathons or coding challenges, and work on small data science projects. Focus on mastering relevant libraries (e.g., Biopython, scikit-learn, TensorFlow/PyTorch).
Tools & Resources
GitHub, Kaggle, CodeChef, GeeksforGeeks, Jupyter Notebooks, Google Colab, Relevant online courses
Career Connection
These technical skills are indispensable for analyzing complex biological data, developing new algorithms, and are highly sought after by biotech, pharma, and tech companies hiring computational biologists and data scientists in India.
Intermediate Stage
Develop a Focused Research Proposal and Initiate Thesis Work- (Year 2-3)
Transition from coursework to intensive research. Work closely with your supervisor to refine your research question, conduct an exhaustive literature review, and develop a detailed, feasible Ph.D. proposal. Begin preliminary experiments, data collection, and analysis, presenting progress regularly.
Tools & Resources
Mendeley/Zotero for referencing, Overleaf for LaTeX, Statistical software (R, SAS), Bioinformatics tools and databases
Career Connection
A well-defined research problem and initial findings are critical milestones for Ph.D. progression and set the stage for high-impact publications, which are vital for both academic and industrial research careers.
Network and Attend Conferences/Workshops- (Year 2-4)
Actively seek opportunities to attend national and international conferences (e.g., ISCB-Asia, BIRD, RECOMB). Present your work (posters/talks), engage with researchers from other institutions, and explore potential collaborations. Participate in workshops to learn new techniques and software relevant to your research.
Tools & Resources
Conference websites, Travel grants from IIIT-Delhi or funding agencies, LinkedIn for professional networking
Career Connection
Networking opens doors to postdoctoral positions, industry collaborations, and provides visibility for your research, accelerating career growth in India''''s scientific community.
Publish Research Findings in Reputable Journals- (Year 3-4)
Prioritize publishing your research work in high-impact, peer-reviewed journals. Learn the scientific writing process, understand journal requirements, and be prepared for rigorous peer review. Aim for at least 2-3 quality publications before thesis submission.
Tools & Resources
Journal submission platforms, Academic writing workshops, Grammarly/similar tools, Feedback from supervisor and peers
Career Connection
Publications are the currency of academic and research careers. They demonstrate your capability as an independent researcher and significantly boost your profile for faculty positions, senior R&D roles, and competitive fellowships.
Advanced Stage
Prepare and Defend Your Ph.D. Thesis- (Year 4-5)
Focus on consolidating your research findings into a comprehensive thesis document. Allocate ample time for writing, editing, and formatting. Practice your thesis defense presentation extensively with your committee and peers, addressing potential questions and refining your arguments.
Tools & Resources
Thesis guidelines from IIIT-Delhi, Supervisor and committee feedback, Mock defense sessions, Presentation software (PowerPoint/Keynote/Beamer)
Career Connection
A successful thesis defense is the culmination of your Ph.D. journey and signifies your readiness for independent research. It''''s a key credential for all advanced scientific roles.
Strategically Plan Post-Ph.D. Career Paths- (Year 4-5)
Begin exploring career options (postdoctoral research, industry R&D, entrepreneurship) well before thesis submission. Tailor your resume/CV and cover letters to specific roles. Prepare for interviews, practice technical questions, and leverage your network for job leads and recommendations within India and abroad.
Tools & Resources
IIIT-Delhi Career Services, LinkedIn, Naukri, Indeed, Company career portals, Informational interviews with professionals
Career Connection
Proactive career planning ensures a smooth transition post-Ph.D., aligning your research expertise with available opportunities in India''''s growing biotech and IT sectors.
Mentor Junior Researchers and Build Leadership Skills- (Year 4-5)
Take initiative to mentor junior Ph.D. or Master''''s students in your lab. Guide them through experimental design, data analysis, and scientific writing. This experience will hone your leadership, communication, and teaching skills, which are invaluable for both academic and managerial roles in research.
Tools & Resources
Lab meetings, One-on-one discussions, Reviewing junior students'''' work, Leadership workshops
Career Connection
Mentorship and leadership skills are highly valued in senior research positions, principal investigator roles, and R&D team leadership, enabling you to contribute to the growth of scientific talent in India.
Program Structure and Curriculum
Eligibility:
- B.Tech/B.E./M.C.A./M.Sc./M.Tech./M.S. in CS/IT/ECE/EE/Maths/Bioinformatics/Computational Biology or related areas. Minimum CGPA of 8.0/10 (B.Tech/B.E.) or 7.5/10 (M.Tech/M.S./M.Sc. with 4-year UG). Valid GATE/UGC-CSIR NET/NBHM or equivalent qualification is generally required, with waivers for exceptional candidates.
Duration: Minimum 3 years, Maximum 7 years (for coursework and thesis submission)
Credits: Minimum 36 credits (for students with B.Tech/M.Tech background) Credits
Assessment: Internal: Varies by course and instructor (typically assignments, quizzes, mid-term exams), External: Varies by course and instructor (typically end-term exams, projects)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BID601 | Advanced Genomics | Elective | 3 | Genome Sequencing Technologies, Computational Gene Annotation, Variant Calling and Interpretation, Comparative Genomics, Transcriptomics and RNA-Seq Analysis |
| CSD605 | Machine Learning | Elective | 3 | Supervised and Unsupervised Learning, Regression and Classification Algorithms, Clustering Techniques, Dimension Reduction, Model Evaluation and Validation |
| BID603 | Systems Biology | Elective | 3 | Biological Networks and Pathways, Mathematical Modeling of Biological Systems, Flux Balance Analysis, Dynamic Systems Modeling, Perturbation Analysis |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CBD601 | Advanced Topics in Computational Biology | Elective | 3 | Single-Cell Omics, Spatial Transcriptomics, AI for Drug Discovery, Personalized Medicine, Ethical AI in Healthcare |
| CSD610 | Deep Learning | Elective | 3 | Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Transformers and Attention Mechanisms, Model Optimization and Regularization |
| Maths 600-700 Level Course (Example) | Advanced Optimization Techniques | Elective | 3 | Convex Optimization, Non-linear Optimization, Combinatorial Optimization, Dynamic Programming, Application in Machine Learning |
| CB/Bio/CS/Maths 600-700 Level Course (Example) | Special Topics in Computational Biology | Elective | 3 | Bioimage Analysis, Computational Neuroscience, Evolutionary Genomics, CRISPR-Cas9 Design and Analysis, Microbiome Data Science |
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSD501 | Research Methodology | Core (Mandatory for all Ph.D. students) | 3 | Problem Formulation and Hypothesis, Literature Review Techniques, Experimental Design and Data Collection, Statistical Analysis and Interpretation, Scientific Writing and Presentation, Research Ethics and Plagiarism |
| HMD501 | Communication Skills | Core (Mandatory for all Ph.D. students) | 3 | Academic Presentation Skills, Technical Report Writing, Research Proposal Development, Interpersonal and Cross-cultural Communication, Public Speaking and Defense Strategies |
| CSD502 | Seminar | Core (Mandatory for all Ph.D. students) | 3 | Literature Survey and Critical Analysis, Research Topic Selection, Developing a Research Outline, Presenting Research Ideas, Engaging in Academic Discussion |
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CB/Bio/CS/Maths 600-700 Level Course 1 (Example) | Advanced Topics in Computational Biology | Core (From designated pool) | 3 | Next-generation Sequencing Analysis, Machine Learning in Genomics, Systems Biology Modeling, Network Biology, Drug Design and Discovery |
| CB/Bio/CS/Maths 600-700 Level Course 2 (Example) | Advanced Machine Learning | Core (From designated pool) | 3 | Deep Learning Architectures, Reinforcement Learning, Probabilistic Graphical Models, Natural Language Processing in Biomedicine, Causal Inference |
