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Education 4.0 is a technique of learning that is connected with the fourth industrial revolution and focuses on transforming the future of education through advanced technology and automation. Smart technology, artificial intelligence and robotics are part of this industrial revolution. Extensive amounts of data gathered from educational setups need to be analyzed using intelligent data analysis (IDA) methods. Smart assistants and support systems need to be developed using such data through advanced techniques of machine learning.

Challenges for Research Interns

1. Use traditional data science and analysis techniques on education-related data
2. Design and implement new techniques for the interpretation of data gathered from live educational environments

Prerequisites

1. Technical Skills
   1. Programming Languages – Python / JavaScript / MATLAB
   2. Libraries – Numpy / Pandas / Tensorflow / Tensorflow.js / PyTorch / Scikit-learn
2. Non-Technical Skills
   1. Good Quantitative Aptitude
   2. Good Qualitative Aptitude
   3. Good Communication Skills

Extensive amounts of data gathered in medical databases request the development of specialized tools for storing, processing and accessing of data. Intelligent data analysis (IDA) methods for clinical datasets provide the means to overcome the resulting gap between data gathering and data comprehension. Intelligent clinical data analysis systems require precise qualitative descriptions of data to enable effective and context sensitive interpretations.

Challenges for Research Interns

1. Use traditional data science and analysis techniques on medical data
2. Design and implement new techniques for the interpretation of large amounts of medical data

Prerequisites

1. Technical Skills
   1. Programming Languages – Python / JavaScript / MATLAB
   2. Libraries – Numpy / Pandas / Tensorflow / Tensorflow.js / PyTorch / Scikit-learn
2. Non-Technical Skills
   1. Good Quantitative Aptitude
   2. Good Qualitative Aptitude
   3. Good Communication Skills

Machine learning allows us to unearth patterns in physiological signals and make healthcare predictions in both daily and clinical situations. This broadens the reach of healthcare from conventional clinical contexts to pervasive everyday scenarios, from passive data collection to active decision-making.

Challenges for Research Interns

1. Define a standard framework for smart healthcare aimed at both daily and clinical settings
2. Investigate state-of-the-art smart healthcare systems and their constituent components
3. Discuss various considerations and challenges that should be taken into account while designing SHS

Prerequisites

1. Technical Skills
   1. Programming Languages – Python / JavaScript / MATLAB
   2. Libraries – Numpy / Pandas / Tensorflow / Tensorflow.js / PyTorch / Scikit-learn
2. Non-Technical Skills
   1. Good Quantitative Aptitude
   2. Good Qualitative Aptitude
   3. Good Communication Skills

Data acquisition is a pivotal building block of the IoT technology. A data acquisition device helps users to make machines smarter by gathering and analyzing real-time data. Numerous IoT based systems are in deployment in the healthcare industry in order to monitor and diagnose the current state and to facilitate the wellbeing of a patient in an autonomous way. However, the reliability and durability of biosignals sensors and devices play vital roles towards establishing an ideal IoT healthcare system. Any variations in biosignals from the normal patterns can help diagnose abnormalities in the human body. Hence, to acquire these biosignals special bio-compatible circuitry and measurement techniques are required. Furthermore, to ensure ubiquitous and reliable data availability, acquired data signals need to be digitized, filtered and processed for the extraction of underlying events and features.

Challenges for Research Interns

1. Design new data acquisition systems using IoT devices
2. Design systems capable of acquiring different types of data, related to person and as a result of data processing be able to extract a series of behavioral patterns

Prerequisites

1. Technical Skills
   1. Programming Languages – C / C++ / Python
   2. Hardware – Arduino, Raspberry Pi, NVIDIA Jetson
2. Non-Technical Skills
   1. Good Quantitative Aptitude
   2. Good Qualitative Aptitude
   3. Good Communication Skills

Novel data analysis and visualization systems for big data need to be introduced in order to improve the user experience in the process of analysis, and improve the intuitiveness of the information represented through the data. The dynamic nature of data from multiple sources continues to provides interpretation challenges to users. Therefore, there is an urgent need to create information rich visualizations for the purpose of gaining insights during research in data-rich domains.

Challenges for Research Interns

1. Design new data analysis and visualization systems for Big Data
2. Represent AI / ML / DL processes using interactive visualizations

Prerequisites

1. Technical Skills
   1. Programming Languages – Python / JavaScript / MATLAB
   2. Libraries – Numpy / Pandas / Tensorflow / Tensorflow.js / PyTorch / Scikit-learn
2. Non-Technical Skills
   1. Good Quantitative Aptitude
   2. Good Qualitative Aptitude
   3. Good Communication Skills

The research at CyPsi lab has always been targeted towards creation of end-user products to enable them. The end-users of a product backed by comprehensive research can either be regular users or future researchers. Hence, we also develop software for a variety of users.

Challenges for Research Interns

1. Design and develop backend services for ML-based web applications
2. Design, develop and maintain previously built web applications
3. Design and develop frontend for web applications

Prerequisites

1. Technical Skills
   1. Programming Languages – Python / PHP / JavaScript / Node
   2. Libraries – Flask / Django / Yii2 / Laravel / React / Angular / Vue
   3. Concepts – MVC / AJAX
2. Non-Technical Skills
   1. Good Quantitative Aptitude
   2. Good Qualitative Aptitude
   3. Good Communication Skills