2023-2024 PhD Projects

Project Overview:

Tissue engineering offers innovative solutions for regenerating or replacing damaged tissues and organs. The traditional top-down approach often struggles to replicate the fine complexity and heterogeneity of native tissues. A bottom-up approach, using microrobots, could overcome these limitations, providing the precision and control required to assemble cellular constructs from the ground up. This PhD project will investigate and develop microrobots designed for bottom-up tissue engineering.

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Project Objectives:

The primary objectives of this research project are:

Design and Development of Microrobots: Create microrobots capable of manipulating individual cells or small cellular clusters. These robots should exhibit biocompatibility, precise maneuverability, and capabilities to perform under biofabrication conditions.

Cell Assembly Techniques: Develop novel techniques and strategies for assembling cells into complex, multi-layered constructs using microrobots. This may involve arranging cells based on cell type, orientation, or spatial arrangement to mimic the native architecture of the target tissue.

Integration of Sensing Mechanisms: Incorporate sensing technologies into the microrobots, enabling them to respond to their microenvironment in real-time and adjust their actions accordingly during the assembly process.

Validation of Engineered Tissues: Test the functionality and viability of the engineered tissues in vitro and in vivo. This includes assessing cell survival, tissue integrity, and biological function, and comparing them to tissues engineered using traditional methods.

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Expected Impact:

The successful completion of this project could revolutionize the field of tissue engineering, providing a novel approach for creating tissues that more accurately mimic their in vivo counterparts. This could significantly enhance the success rate of tissue-engineered implants and pave the way for the fabrication of more complex tissues or even whole organs.

This project offers an exciting opportunity for the PhD candidate to work at the cutting edge of tissue engineering and microrobotics, two rapidly advancing fields with the potential to significantly impact healthcare. The skills and knowledge acquired will be highly relevant for a variety of career paths, from academic research to biotechnology development.

Autonomous Micro/Nano-Robots for Targeted Treatment

Project Overview:

The advancement of autonomous micro/nano-robots offers the potential to transform targeted treatment strategies, especially in challenging fields like oncology, neurology, and cardiovascular diseases. These ultra-small robots can be navigated through the body to deliver drugs directly to disease sites, improving treatment efficacy while reducing systemic side effects. This PhD project aims to explore and develop autonomous micro/nano-robots that can perform targeted treatment in complex physiological environments.

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Project Objectives:

The primary objectives of this research project are:

Design and Fabrication of Micro/Nano-Robots: Create biocompatible, maneuverable micro/nano-robots capable of carrying and releasing therapeutic payloads. The robots should have the ability to navigate through various body fluids and tissues to reach targeted sites.

Autonomous Navigation and Control: Develop advanced control algorithms for autonomous navigation and decision-making based on sensory feedback. This involves integrating machine learning and AI techniques with real-time data interpretation.

Therapeutic Payload Integration: Incorporate methods for loading therapeutic payloads onto the robots and controlled release mechanisms at the targeted site. This could involve chemotherapeutic agents for cancer, clot-dissolving drugs for thrombosis, or neuroprotective drugs for neurodegenerative diseases.

In Vitro and In Vivo Validation: Conduct rigorous testing of the micro/nano-robots in vitro (e.g., in cell cultures or organ-on-a-chip systems) and in vivo (e.g., in animal models) to evaluate their navigation capabilities, safety, and therapeutic efficacy.

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Expected Impact:

The successful completion of this project has the potential to revolutionize targeted treatment strategies. By allowing for precise, localized delivery of therapies, autonomous micro/nano-robots can significantly increase treatment efficacy and reduce adverse side effects, leading to improved patient outcomes.

This PhD project offers the candidate an exciting opportunity to work at the cutting edge of medical robotics, nanotechnology, and targeted therapy. The acquired knowledge and skills will be highly valuable for a variety of career paths in academic research, healthcare technology, and pharmaceuticals.

Machine Learning-Based Perception of Microscopic Images for Biomedical Applications

Project Overview:

Microscopic images are an invaluable tool in biomedical research and clinical diagnostics. However, the wealth of information that these images contain can be difficult to interpret due to factors such as high-dimensionality, noise, and variability. This PhD project aims to harness the power of machine learning to enhance the perception and interpretation of microscopic images in biomedical applications, driving forward our capabilities in disease diagnosis, drug development, and understanding of biological processes.

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Project Objectives:

The primary objectives of this research project are:

Challenges in Biomedical Microscopy: Investigate the unique challenges in interpreting microscopic images in the biomedical context, including issues related to biological variability, sample preparation artefacts, noise, and the need for precise and reliable quantification.

Develop Machine Learning Models: Design and implement machine learning algorithms tailored for the challenges and needs of microscopic image analysis in biomedicine. This could involve supervised learning for diagnostic tasks, unsupervised learning for discovery of unknown patterns, and reinforcement learning for sequential decision-making in image analysis.

Enhance Perception of Biomedical Microscopic Images: Apply the developed machine learning models to enhance the interpretation of biomedical microscopic images. This could involve tasks such as cell identification and classification, disease marker recognition, or morphological analysis.

Validation and Evaluation: Validate and test the developed algorithms using real-world biomedical microscopic data sets. This should include quantitative measures of performance and comparison with traditional image analysis methods.

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Expected Impact:

The successful completion of this project could significantly advance the field of biomedical microscopy. Machine learning algorithms tailored to microscopic image analysis could provide more accurate and efficient analysis, accelerating biomedical research and improving clinical diagnostics.

This PhD project provides an exciting opportunity for the candidate to work at the intersection of machine learning, microscopy, and biomedicine. The skills and knowledge gained will be highly relevant for a range of career paths in academia, healthcare technology, biomedical research, and data science.