I’d like to share a personal story that highlights the challenges and valuable lessons I learned during my PhD proposal presentation to the university panel. It was a pivotal moment in my academic journey, one that taught me the importance of preparation, adaptability, and resilience.
Fresh off completing my Master of Technology (MTech), I made the ambitious decision to pursue a PhD. Eager to build upon the foundation laid by my previous project, I chose to continue exploring the problem statement that had captivated my interest.
Determined to leave no stone unturned, I meticulously prepared my presentation, meticulously crafting over 100 slides packed with intricate details. However, in my enthusiasm to cover every aspect of my research, I overlooked a crucial factor: time management.
As I stood before the panel, ready to deliver my meticulously prepared presentation, I quickly realized that I had underestimated the constraints of time. Within minutes, the panel members interrupted, kindly but firmly asking me to halt my presentation after only 10 minutes.
This unexpected setback left me feeling disheartened and flustered. I had assumed that the panel members were experts in my field, familiar with the intricacies of the technology I was discussing. However, I soon discovered that they represented a diverse range of backgrounds, including policymakers with limited technical knowledge.
Struggling to convey complex concepts in a language accessible to all, I found myself reverting to my mother tongue, further complicating the flow of my presentation. It became evident that I had failed to communicate my data collection strategies and methodology effectively, leaving panel members with unanswered questions.
Yet, despite these challenges, I was met with a surprising level of receptivity from the panel members. Rather than dismissing my shortcomings, they patiently listened to my presentation, offering constructive feedback and guidance along the way.
In particular, the policymakers played a pivotal role in helping me align my objectives with the needs of society, emphasizing the importance of making my research relevant and impactful. Their insights prompted me to reconsider my approach and develop a contingency plan, ensuring that I was prepared for any challenges that might arise during implementation.
Despite the setbacks, the panel members recognized my potential and allowed me to register for my PhD, offering me the opportunity to continue my academic journey. Their belief in my abilities served as a source of encouragement, motivating me to redouble my efforts and strive for excellence in my research endeavours.
In the end, while my PhD proposal presentation may not have gone as smoothly as I had hoped, it was a humbling and enlightening experience. It taught me the importance of humility, adaptability, and perseverance in the face of adversity.
Armed with the valuable feedback and guidance provided by the panel members, I emerged from the experience with a newfound sense of clarity and purpose. I am grateful for the lessons learned and the opportunity to grow as both a researcher and a person.
Introduction
As part of the Ph.D. selection process, all students are required to present their Ph.D. proposal for approval to the Ph.D. Scrutiny Committee at the University. The goal of the Ph.D. proposal presentation and approval process is to receive constructive feedback on the proposal and ensure that the Ph.D. proposal is feasible and appropriate for Ph.D. work. The panel also can look into the timeline of the proposed work to ensure its feasibility within the given time frame. Above all, it gives an opportunity to the research scholar to face the panel during the Ph.D. proposal presentation at the early stage of his research.
Please note, before making the presentation you need to submit the 10-12 page PhD proposal Report to the University and then make a presentation in front of the selection panel. The selection panel will go through both your report and presentation to make selection. If you are not familiar with writing PhD proposal report, please visit my blog post on “Writing PhD Proposal Report to the University” for a clear understanding of how to write the PhD proposal report in a a concise and professional manner.
Format of Ph.D. Proposal Presentation
The time duration of the presentation will be around 15-20 minutes. The presentation slides should be simple, well-structured, and effective.
The presentation slides should include the following:
- The Title of the work along with the candidate and supervisor details along with their affiliations.
- Introduction to the proposal
- A brief review of relevant literature
- Motivation for the work
- Statement of the research problem and goals
- Research question, objectives of the proposal
- Study design, methods for data collection, measures
- Predicted outcomes if everything goes according to plan
- Resources to complete the work
- Societal impact
- A timetable of activities (Gantt Chart)
- Potential challenges
Maintaining the time limitation of the Ph.D. proposal presentation is crucial otherwise the panel members may stop the presentation after the time limit and the candidate may lose his chance to clearly explain the idea.
After the Ph.D. proposal presentation, the candidate has to face the panel for clearing their doubts regarding the proposal. For this session to run smoothly, prior to this presentation the candidate has to present his work to his guide and other fellow researchers of his choice several times to get acquainted with the concepts and queries.
During the discussion, the panel may ask the following questions to the candidate
- What is the (social, scientific) significance of the proposal?
- How will you approach your research question?
- Is your proposal novel? How is it related/compared to prior works?
- What difficulties do you expect to encounter during the implementation?
- What will be the impact of this proposal on research/society?
- Show the sample of data you are planning to collect.
- What research has already been done in the proposed area? What deficiencies or gaps need attention?
- In the proposed domain, can you list the other ongoing research works?
- Why do you think your research is reliable?
- Why do you think your research is valid?
- How do you validate your outcomes?
- In what way(s) does your research proposal contributes to knowledge?
- What research methodology do you use?
- Why did you use a particular research methodology?
- Can you bridge any gap in your work?
- What are the limitations of the proposal?
- Which programming language will you use to write your program? (for computer science students)
- What source of data will be employed for the research? whether you are data is benchmarked?
- Have you taken permission to use the data set you are planning to use in your research?
- What is the strongest point in your proposal?
- In what way your research is environment friendly?
- Suppose the proposed method does not work then what alternate solution you have planned for?
- Who are the experts you are in contact with in the domain you are working?
- What are the gaps you have identified in paper XYZ shown in your references?
- How is your method better than the method proposed in paper PQR?
Points to Ponder During Ph.D. Proposal Presentation
During the Ph.D. proposal presentation, the following points should be given prime importance
- Use simple color combinations (contrasting colors) for your slides
- Make eye contact with your panel members
- Do not have any other personal material on the pen drive or External Hard Disk in which you carry your presentation
- Do not write an entire paragraph in slides.
- Add a story to your presentation. This story which you will discuss can be a problem you have seen in a specific domain where you are planning to work and explain how your research proposal may solve that problem.
- Do not start teaching the basic concepts. The panel members already know the basic concepts. Only concentrate on objectives and methodology.
- Start your presentation by disclosing a surprising /shocking fact, about the work you are considering. This will create interest in the panel members
- Highlight the papers presented/ workshops attended by you relating to your research.
- Acknowledge the domain experts with whom you are interacting to collect the data sets ( This will indirectly show the quality of the data sets you are planning to use ).
- Use pause in between your presentation. A pause is an effective way to grab attention.
- Offer alternative solutions/backup plans for your research work.
- Do not cross the time limit
- Have Backup slides
- If you do not know the answer to any of the questions say confidently that you have not come across that concept or you do not have a clear idea regarding the same. Do not bluff. This may leave a wrong impression on the panel.
Ph.D. Proposal Presentation Template
Slide 1: Title Slide
- Title of the work
- Candidate’s name and affiliation
- Supervisor’s name and affiliation
Slide 2: Introduction
- Briefly introduce the topic
- Explain why the topic is important and relevant
- Provide a brief overview of what the presentation will cover
Slide 3: Literature Review
- Summarize the key findings of relevant literature
- Identify gaps and limitations in the existing research
- Explain how your work will contribute to filling these gaps
Slide 4: Motivation and Research Problem
- Explain the motivation behind your work
- Clearly state the research problem you are addressing
Slide 5: Research Question and Objectives
- State your research question
- Clearly articulate your research objectives
Slide 6: Study Design and Methods
- Explain your study design and why you chose it
- Describe your data collection methods and measures
Slide 7: Predicted Outcomes
- Present your predicted outcomes if everything goes according to plan
- Explain how these outcomes will contribute to the field
Slide 8: Resources
- Identify the resources you will need to complete your work
- Explain how you will obtain these resources
Slide 9: Societal Impact
- Describe the potential societal impact of your work
- Explain how your work will benefit society
Slide 10: Gantt Chart
- Present a Gantt chart representing the timetable of the activities planned
- Explain how you will manage your time to complete your work on schedule
Slide 11: Potential Challenges
- Identify potential challenges you may encounter during your research
- Explain how you plan to address these challenges
Slide 12: Conclusion
- Summarize the key points of your presentation
- Conclude by emphasizing the significance of your work and its potential impact
Slide 13: Questions
- Encourage the audience to ask questions
- Thank the audience for their attention
Remember to keep your presentation simple, well-structured, and effective. Use clear and concise language, and make sure your presentation is visually engaging. Good luck with your PhD proposal presentation!
Slide 1: Title Slide
- Title of the work: “A Comparative Study of Deep Learning Techniques for Image Recognition in Medical Imaging”
- Candidate’s name and affiliation: Sarah Johnson, Department of Computer Science, University of ABC
- Supervisor’s name and affiliation: Dr. Robert Lee, Department of Computer Science, University of ABC
In this slide, you have to include the title of your work, your name and affiliation as the PhD candidate, and your supervisor’s name and affiliation. The title should be concise and descriptive, conveying the essence of your research.
Slide 2: Introduction
- Briefly introduce the topic: Deep Learning Techniques for Image Recognition in Medical Imaging
- Explain why the topic is important and relevant: Accurate and efficient image recognition in medical imaging is crucial for diagnosis, treatment planning, and monitoring of patient progress. However, the current state-of-the-art algorithms still have limitations in handling the complexities of medical images, such as noise, variation in size and shape, and variation in imaging protocols.
- Provide a brief overview of what the presentation will cover: In this presentation, I will introduce my proposed research on a comparative study of deep learning techniques for image recognition in medical imaging. I will briefly cover the literature review, the research problem and goals, the study design, and the expected outcomes of the research.
In this slide, you have to provide an introduction to your research topic, explaining its importance and relevance in the field. The introduction should set the context for your research and explain why it matters.
Slide 3: Literature Review
- Summarize the key findings of relevant literature: Previous research has shown that deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have achieved state-of-the-art results in various image recognition tasks, including medical image recognition. However, the performance of these techniques can be affected by factors such as the size and complexity of the dataset, the selection of hyperparameters, and the choice of architecture.
- Identify gaps and limitations in the existing research: While previous studies have compared the performance of different deep learning techniques for image recognition in general, there is a lack of research that compares and evaluates the performance of these techniques specifically in medical imaging. Additionally, there is a need for research that investigates the effectiveness of transfer learning, data augmentation, and other techniques for improving the performance of deep learning models in medical image recognition tasks.
- Explain how your work will contribute to filling these gaps: The proposed research aims to contribute to filling these gaps by conducting a comparative study of various deep learning techniques for image recognition in medical imaging. The study will also investigate the effectiveness of transfer learning, data augmentation, and other techniques for improving the performance of these techniques in medical image recognition tasks. The results of this study will provide valuable insights into the strengths and limitations of different deep-learning techniques in medical imaging, and help inform the development of more accurate and efficient algorithms in the future.
In this slide, you have to summarize the key findings of relevant literature in your research area, identify gaps and limitations in the existing research, and explain how your work will contribute to filling these gaps.
| Slide 3: Literature Review |
|---|
| Key Findings |
| – Deep learning techniques (e.g. CNNs, RNNs) have achieved state-of-the-art results in various image recognition tasks, including medical image recognition. |
| – Performance can be affected by factors such as dataset size and complexity, hyperparameter selection, and architecture choice. |
| Gaps & Limitations |
| – Lack of research comparing and evaluating deep learning techniques specifically in medical imaging. |
| – Need for investigation of transfer learning, data augmentation, and other techniques for improving deep learning model performance in medical image recognition tasks. |
| Contribution of Proposed Work |
| – Conduct a comparative study of various deep learning techniques for image recognition in medical imaging. |
| – Investigate the effectiveness of transfer learning, data augmentation, and other techniques for improving deep learning model performance in medical image recognition tasks. |
| – Provide valuable insights into the strengths and limitations of different deep learning techniques in medical imaging, and help inform the development of more accurate and efficient algorithms in the future. |
In this format, the information is organized into three sections: key findings, gaps and limitations, and contribution of proposed work. Each section is presented as a bullet point, with the main idea in bold, followed by a brief explanation. This format can be useful for presenting information in a clear and concise manner, while still providing enough detail to convey the main points.
Slide 4: Motivation and Research Problem
| Slide 4: Motivation and Research Problem |
|---|
| Motivation |
| – Medical image recognition is an important application with significant potential for improving patient outcomes. |
| – Deep learning techniques have shown promise in this area, but their effectiveness depends on various factors, and there is still room for improvement. |
| – A comprehensive study of deep learning techniques for medical image recognition could help identify the most effective approaches and guide future research. |
| Research Problem |
| – The goal of this research is to conduct a comparative study of deep learning techniques for image recognition in medical imaging and investigate the effectiveness of transfer learning, data augmentation, and other techniques for improving model performance. |
| – Specifically, we aim to address the following research questions: |
| – What are the relative strengths and weaknesses of different deep-learning techniques for medical image recognition? |
| – How can transfer learning and data augmentation be used to improve model performance? |
| – What are the key factors affecting model performance, and how can they be optimized? |
In this format, the motivation and research problem are presented as two separate sections, with each section consisting of bullet points. The motivation section explains why the topic is important and why the proposed research is needed, while the research problem section clearly states the specific questions that the research will address. This format can help ensure that the motivation and research problem are clearly articulated and easy to understand.
Slide 5: Research Question and Objectives
| Slide 5: Research Question and Objectives |
|---|
| Research Question |
| – What are the most effective deep-learning techniques for medical image recognition, and how can they be optimized for improved performance? |
| Research Objectives |
| – To conduct a comparative study of deep learning techniques for image recognition in medical imaging, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models. |
| – To investigate the effectiveness of transfer learning, data augmentation, and other techniques for improving model performance. |
| – To identify the key factors affecting model performance, including dataset size, complexity, and quality, and optimize these factors for improved accuracy and efficiency. |
| – To develop a comprehensive set of guidelines for using deep learning techniques in medical image recognition, based on the results of the study. |
In this format, the research question and research objectives are presented as two separate sections, with each section consisting of bullet points. The research question clearly states the specific problem that the research will address, while the research objectives explain the specific goals that the research aims to achieve in order to answer the research question. This format can help ensure that the research question and objectives are clearly articulated and easy to understand.
Slide 6: Study Design and Methods
| Slide 6: Study Design and Methods |
|---|
| Study Design |
| – Comparative study of deep learning techniques for medical image recognition. |
| – Experimental design with three groups: one using convolutional neural networks (CNNs), one using recurrent neural networks (RNNs), and one using hybrid models. |
| – Randomized assignment of datasets to groups to control for confounding factors. |
| Data Collection Methods |
| – Datasets: Publicly available medical image datasets, including the MURA, ChestX-ray8, and DeepLesion datasets. |
| – Measures: Accuracy, sensitivity, specificity, and AUC for image recognition. |
| – Methods: Each group will train and test their models on the same datasets, with performance measures recorded for each model. |
In this format, the study design and data collection methods are presented as two separate sections, with each section consisting of bullet points. The study design section provides an overview of the design of the study, including the specific groups being compared and the methods used to control for confounding factors. The data collection methods section describes the datasets and measures being used, as well as the specific methods being employed to train and test the deep learning models. This format can help ensure that the study design and methods are clearly explained and easy to understand.
Slide 7: Predicted Outcomes
| Slide 7: Predicted Outcomes |
|---|
| Predicted Outcomes |
| – The CNN group is predicted to achieve the highest accuracy and AUC scores for medical image recognition. |
| – The hybrid model group is predicted to achieve high sensitivity and specificity scores, making it well-suited for certain medical applications. |
| – The RNN group is predicted to perform well on image sequences, such as those in medical videos or time-lapse images. |
| Contribution to the Field |
| – This study will provide a comparative analysis of deep learning techniques for medical image recognition, helping to identify which techniques are most effective for different applications. |
| – The study will contribute to the development of improved medical image recognition models, which can have a significant impact on patient care and treatment outcomes. |
In this format, the predicted outcomes are presented as bullet points, along with an explanation of how they will contribute to the field. The predicted outcomes are based on the study design and methods described in previous slides and can help to demonstrate the potential impact of the proposed research.
Slide 8: Resources
| Slide 8: Resources |
|---|
| Resources Needed |
| – Access to medical image databases with labeled images for model training and testing. |
| – Powerful computing resources, such as GPUs, for running deep learning algorithms. |
| – Software tools for image pre-processing, deep learning model training, and model evaluation. |
| – Technical support for troubleshooting and optimizing software and hardware issues. |
| Obtaining Resources |
| – Medical image databases will be obtained through collaborations with healthcare institutions and research organizations. |
| – Computing resources will be obtained through the university’s high-performance computing center. |
| – Software tools will be obtained through open-source repositories and commercial licenses as needed. |
| – Technical support will be provided by the university’s IT department and by contacting software vendors and community forums as needed. |
This slide presents the resources needed to complete the work, along with an explanation of how these resources will be obtained. This can help to demonstrate that the necessary resources have been identified and that a plan is in place to obtain them.
Slide 9: Societal Impact
| Slide 9: Societal Impact |
|---|
| Potential Societal Impact |
| – Improving the accuracy and efficiency of medical image analysis can lead to more accurate and timely diagnoses, which can improve patient outcomes and reduce healthcare costs. |
| – Developing robust and interpretable deep learning models can help to build trust in these technologies and enable their widespread adoption in clinical practice. |
| – Generating new insights into brain tumor growth and progression can help to guide treatment decisions and lead to more personalized and effective therapies. |
| How Work Will Benefit Society |
| – By improving medical image analysis, our work can help to reduce the time and cost of diagnosis, increase the accuracy of treatment planning, and ultimately improve patient outcomes. |
| – By developing more interpretable and trustworthy deep learning models, our work can help to facilitate their integration into clinical practice and improve patient care. |
| – By providing new insights into brain tumor growth and progression, our work can help to guide the development of more targeted and effective treatments. |
This slide presents the potential societal impact of the work and how it will benefit society. This can help to demonstrate the broader implications and significance of the research.
Slide 10: Gantt Chart
Gnatt chart representing the timetable of the activities planned
You have to create a Gantt chart to represent the activities that are planned for completing this research work within the given time frame. The time frame can change depending on the Univesity’s stipulated guidelines for full-time and part-time Ph.D. programs.
The chart is divided into five different stages, which are:
- Completion of the Course Work: You need to complete the coursework papers as per University Guidelines. This stage is expected to take 12 months.
- Literature review: In this stage, we will review and analyze the existing literature to identify gaps and limitations in the research. This stage is expected to take 06 months.
- Data collection: In this stage, we will collect the required data by conducting experiments and surveys. This stage is expected to take 06 months.
- Data analysis: In this stage, we will analyze the collected data to draw meaningful insights and conclusions. This stage is expected to take 3 months.
- Model development: In this stage, we will develop the proposed model and implement it. This stage is expected to take 12 months.
- Results and Analysis: In this stage, we will gather the results from various dimensions of the proposed model and analyze them. This stage is expected to take 03 months.
- Writing and submission: In this stage, we will write and submit the final research report and the thesis. This stage is expected to take 06 months.
You have to allocate appropriate time for each stage to complete the work on schedule. You have to keep track of the progress regularly and make necessary adjustments to the plan to ensure the timely completion of the research work.
Slide 11: Potential Challenges
In this section, you have to discuss some potential challenges which you may encounter during your research and how you plan to address them.
Potential Challenges:
- Access to data: Since we are planning to collect data from several sources, it may be challenging to obtain access to all the necessary data.
- Time constraints: We have a strict timeline to follow, and any delays could affect the overall success of the project.
- Technical difficulties: There is always a risk of encountering technical difficulties during data collection or analysis.
Addressing the Challenges:
- Data access: We will communicate with the relevant authorities and request access to the data needed for our research. We will also explore alternative sources of data if necessary.
- Time constraints: We will break down our research into smaller, more manageable tasks and allocate sufficient time for each. We will also build in extra time in case of unexpected delays.
- Technical difficulties: We will test our data collection and analysis tools thoroughly beforehand to minimize the risk of technical difficulties. We will also have contingency plans in place in case of any issues that may arise.
By identifying potential challenges and having a plan in place to address them, you can ensure that your research progresses smoothly and efficiently.
Slide 12: Conclusion
In conclusion, this presentation has outlined a research proposal for a comparative study of deep learning techniques for image recognition in medical imaging. The key points covered in this presentation are:
- The importance of developing accurate and efficient image recognition techniques for medical imaging, which can assist in the diagnosis and treatment of various medical conditions
- A review of the relevant literature in this field has identified the need for further research to compare the performance of different deep-learning techniques for image recognition in medical imaging
- The research problem, objectives, and research question, aim to address this need by comparing the performance of different deep-learning techniques for image recognition in medical imaging
- The study design and methods, which will involve collecting and analyzing medical imaging data using various deep-learning techniques
- The predicted outcomes of the study, which could contribute to improving the accuracy and efficiency of image recognition in medical imaging
- The resources required to complete the study, including access to medical imaging data and computational resources
- The potential societal impact of the study, which could benefit patients and healthcare providers by improving the accuracy and efficiency of medical imaging
- The timetable of activities, which has been represented in a Gantt chart to ensure that the study is completed on schedule
- The potential challenges that may be encountered during the research, and the strategies that will be used to address these challenges.
Overall, this research proposal has the potential to contribute to the field of medical imaging by providing valuable insights into the performance of different deep-learning techniques for image recognition. By improving the accuracy and efficiency of image recognition in medical imaging, this research could ultimately benefit patients and healthcare providers.
Ph.D. Proposal Presentation PPT Download
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How to Convert My Ph.D. Proposal Presentation to a Survey Paper?
Here is an interesting thing. You may be wondering about the amount of effort you have put into preparing the Ph.D. proposal material and its further usage. Here is a quick tip. In fact, after finishing my Ph.D. proposal presentation my supervisor asked me to convert that material into a survey paper so that it can be showcased in the first Doctoral committee meeting to gain some brownie points from the members. I did the same and got lots of admiration from the committee members.
To convert your Ph.D. proposal material to a survey paper, you can start by using your existing literature review as the foundation. Expand your literature review to include a broader range of sources and provide a comprehensive overview of the research area. Use your research question and objectives to structure your paper and provide a detailed analysis of existing research, highlighting gaps and potential areas for future research.
Check out our blog posts listed below on how to write a survey paper and a structured literature review for more guidance on structuring and writing your paper.
How to write a better Survey Paper in 06 easy steps?
The Art of Conducting a Systematic Literature Review (SLR): Expert Advice for Researchers
Unlock Exclusive Access to the PhD Navigator Tool – for a Streamlined Research Experience for FREE!
Dear fellow researchers,
If you are a PhD research scholar or planning to pursue PhD, I understand the value of time in your PhD journey. That’s why I have organized my blog posts related to PhD meticulously, categorizing more than 100 articles into various stages of PhD (from planning of PhD to careers after PhD).
You can get this tool ABSOLUTELY FREE, by sending an email to [email protected] with the subject line “Subscribe: PhD Navigator Tool-1.0” By subscribing not only will you gain free access to this invaluable tool, but you’ll also receive regular updates on this tool and our blog’s latest insights, tips, and resources tailored for researchers.
Happy researching!
Best regards,
Dr Vijay Rajpurohit
Conclusion
A Ph.D. proposal presentation is a crucial step in obtaining approval for your research project. It requires careful planning, organization, and presentation skills to effectively communicate the significance, goals, and methods of your proposed research to the review committee.
By following the tips and guidelines discussed in this blog post, you can create an impressive and compelling presentation that showcases your expertise and potential to make a significant contribution to your field of study.
Remember to emphasize the importance and potential impact of your research, address potential challenges, and provide a clear timeline and plan for your project.
With a well-prepared presentation, you can increase your chances of obtaining approval for your Ph.D. proposal and embarking on a successful research journey.
Frequently Asked Questions
As a new Ph.D. student, how can I improve my presentation skills for a Ph.D. proposal presentation?
To get yourself accepted by the Ph.D. panel you need to do lots of research regarding the domain of interest in which you plan to pursue your Ph.D. Read the base paper thoroughly so that you will be clear regarding the basic implementation details. You need to do lots of rehearsals in front of your friends and family members, and in front of the mirror.
How should Ph.D. students overcome the fear and anxiety of giving a Ph.D. proposal presentation?
By improving their domain knowledge; interacting with domain experts; listening to podcasts and youtube videos related to the concerned domain; and honing their communication skills, Ph.D. students can overcome fear and anxiety while giving the presentation.
What are the most common reasons for rejecting a Ph.D. proposal?
The main reasons for rejecting the proposal are the limited literature survey; incomplete research gap analysis of the domain; non-coherent objectives; and the poor link between the aim and the objectives.
What kind of profile is required to get into top Ph.D. programs?
One or two good publications or conference presentations in the related domain of research will boost the chances of getting into top Ph.D. programs.
Is it very essential to have publications for getting accepted to the Ph.D. program?
It is not essential to have publications for getting accepted to the Ph.D. programs. With thorough knowledge of the domain of research and clearly defined aims and objectives, one can impress the research panel to consider the applicant for the PhD admission.
