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2023-2024 Internship

You are welcome to apply for internship in our lab!


Towards Next-Generation Artificial Intelligence through NeuroAI

Project Overview:

The intersection of neuroscience and artificial intelligence (AI), known as NeuroAI, represents a burgeoning field with the potential to revolutionize our understanding of both neural networks and AI systems. This project aims to leverage insights from the intricate functioning of the brain to design advanced AI algorithms and architectures, potentially leading to the next generation of AI systems.

Project Objectives:

The primary objectives of this research project are:

Neuroscience-AI Integration: Develop an in-depth understanding of the principles and mechanisms underlying the brain's information-processing abilities. Use these insights to inform the development of novel AI algorithms and architectures that mimic these processes.

AI Model Development: Design and implement advanced AI models inspired by neural processes, such as learning, memory formation, and decision-making. These models should demonstrate enhanced learning efficiency, adaptability, and robustness compared to traditional AI models.

Benchmarking and Optimization: Conduct rigorous testing and validation of the NeuroAI models across various tasks and datasets. Use these evaluations to fine-tune the models and improve their performance and reliability.

Real-world Application: Apply the developed NeuroAI models to real-world problems, ranging from image and speech recognition to natural language understanding and autonomous systems. Evaluate the models' performance and practicality in these applications.

Expected Impact:

The successful completion of this project is anticipated to significantly contribute to the fields of AI and neuroscience. By developing AI systems that better mimic the brain's processing capabilities, we can enhance the performance, versatility, and efficiency of AI systems.

Moreover, the project will likely lead to an improved understanding of neural processing and cognition, potentially yielding novel insights into the workings of the brain.

The project offers the candidate the opportunity to work at the cutting edge of neuroscience and AI, both of which are rapidly growing fields with significant future potential. The knowledge and skills acquired will be invaluable in a variety of career paths, including academic research, AI development, and neurotechnology.


Advanced Digital Technology for Personalized Home-Centric Healthcare

Project Overview:

As the demand for personalized and convenient healthcare solutions grows, there is an increasing need to explore how advanced digital technologies can facilitate home-centric healthcare delivery. This project aims to investigate, develop, and validate digital technologies intended for personalized healthcare services delivered within the home setting.


Project Objectives:

The primary objectives of this research project are:

Exploring Advanced Digital Technologies: Investigate various digital technologies like the Internet of Things (IoT), machine learning, artificial intelligence (AI), telemedicine, and mobile health (mHealth) applications and their potential roles in home-centric healthcare.

Design and Development of Personalized Solutions: Develop digital solutions that are personalized to meet individual patient's needs. This could involve creating intuitive mHealth applications, developing sophisticated wearable devices, or designing AI-based prediction models for early disease detection and monitoring.

Integration and Interoperability: Ensure that the developed digital technologies can be seamlessly integrated with existing healthcare systems and are interoperable with other digital health technologies. This should facilitate efficient data sharing and coordination among healthcare providers, patients, and caregivers.

Validation and User Acceptance Testing: Conduct thorough testing of the developed technologies in real-world home settings, measuring their effectiveness, usability, and acceptability among patients and healthcare providers.


Expected Impact:

The successful completion of this project could greatly contribute to the evolution of healthcare delivery, transitioning from traditional clinic-centric models to more patient-friendly, home-based care. Advanced digital technologies promise to enhance accessibility, convenience, and personalization of healthcare services, which can lead to improved patient satisfaction, better health outcomes, and overall cost reduction in healthcare.

This project offers a unique opportunity for the candidate to be at the forefront of healthcare technology innovation. The skills and knowledge gained during this project will be invaluable for a career in academia, health technology development, or within the healthcare sector itself.


Affordable Intelligent Robotics for Home-Care Applications

Project Overview:

The integration of intelligent robotics into home-care services promises to revolutionize personal healthcare, improving the quality of life for those in need of assistance and alleviating the burden on caregivers. However, cost can be a significant barrier to adoption. This project aims to develop affordable intelligent robotics solutions for home-care applications, making home-care robotics more accessible to a broader population.


Project Objectives:

The primary objectives of this research project are:

Understand Home-Care Needs: Conduct a comprehensive study of the needs and requirements of home-care recipients and caregivers. This will involve investigating common tasks and challenges in home-care environments and determining how robotics could address these.

Design and Development of Affordable Robotic Solutions: Leverage cost-effective hardware and open-source software to design and develop intelligent robotics solutions for home-care. This involves creating a versatile robot design that can perform a wide range of home-care tasks and developing intelligent algorithms to enable autonomous or semi-autonomous operation.

Intelligent Algorithms for Home-Care Robotics: Develop advanced machine learning and artificial intelligence algorithms for task understanding, navigation, manipulation, and interaction in a home environment.

User Experience and Acceptance Testing: Test the developed robotic solutions in real or simulated home-care environments, and conduct user experience studies to evaluate the robot's performance, user-friendliness, and acceptance among potential users.


Expected Impact:

The successful completion of this project could make a significant contribution to the field of home-care robotics. By creating affordable, intelligent, and user-friendly robotics solutions, we can make home-care robots more accessible, thereby improving the quality of life for those in need of home-care services and reducing the burden on caregivers.

This project offers the opportunity for the candidate to work at the intersection of robotics, machine learning, and healthcare, providing them with valuable skills and knowledge that can be applied in academic research, the healthcare sector, and the robotics industry.


Sim-to-Real Transfer Learning for Dexterous Robotic Manipulation

Project Overview:

Robotic manipulation tasks have witnessed substantial advancement in recent years, particularly with the rise of deep reinforcement learning methods. However, transferring the learned policies from simulation to real-world environments poses significant challenges due to the "reality gap" — the difference between the physics of the simulated world and the real world. This project aims to explore and improve upon methods for Sim-to-Real Transfer Learning, to enhance the effectiveness of dexterous robotic manipulation in real-world applications.

Project Objectives:

The primary objectives of this research are:

Exploration of the Sim-to-Real Gap: Understand the inherent challenges and nuances of transferring learned behaviors from a simulated environment to the real world. The project will focus on dexterous manipulation tasks which involve complex physical interactions.

Development of Transfer Learning Methods: Develop and test novel algorithms and methodologies for Sim-to-Real transfer. This might involve incorporating domain adaptation techniques, uncertainty quantification, or other strategies to account for the differences between simulation and reality.

Testing and Validation: Implement the developed methods on actual robotic systems and compare the results with those obtained in the simulation. The aim is to validate the effectiveness of the proposed methods, measure the reduction of the reality gap, and analyze the performance of dexterous manipulation tasks.

Real-world Applications: Apply the developed methods to real-world problems, such as manufacturing assembly lines or household tasks, which require sophisticated and dexterous manipulation skills.

Expected Impact:

The successful completion of this project could greatly advance the field of robotics, particularly in tasks requiring dexterous manipulation. It could lead to more robust and efficient learning methods, enabling robots to perform complex tasks more effectively in real-world environments.

The project provides the candidate with a unique opportunity to work at the forefront of robotics and machine learning, preparing them for a variety of career paths in academia, advanced manufacturing, and AI development.

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