Browse Earth and Space Science and Engineering 2024 Research Projects
The LURA and NSERC USRA Summer 2024 Research Program competition is now closed. Applicants will be notified of results by April 1, 2024.
Professor: Jinjun Shan
Contact Info: jjshan@yorku.ca
Lab Website: https://lassonde.yorku.ca/users/jjshan
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Autonomous Unmanned Vehicles (AUVs) are systems that are capable of maneuvering in the air, on the ground, above or under the water. There are a number of potential applications for AUVs in civilian, military, and security areas, for example, defense patrol duties, agricultural activities, forest fire monitoring and control, grid monitoring, boarder control, search, surveillance, and rescue. AUVs have great potential benefits to Canada for numerous reasons; many of which are connected with our large uninhabited land, the largest area of forests in the world, and the longest international border in the world.
This project is to develop control and navigation algorithms for autonomous unmanned vehicles (drones, mobile robots, and self-driving cars) for indoor surveillance and monitoring applications.
Duties and Responsibilities: The successful students will be working with graduate students and research fellows on (a) programming; (b) hardware development and implementation. Through these activities, the students will gain experience in control and navigation system design, hardware, and software development, etc. These experiences will be very helpful for the student’s future study and work.
Desired Technical Skills: Good programming skills, MATLAB, C, and Linux, enrolled in an engineering degree, familiar with ROS, and a team player.
Desired Course(s): Senior engineering students.
Other Desired Qualifications: None.
Contact Info: jjshan@yorku.ca
Lab Website: https://lassonde.yorku.ca/users/jjshan
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Autonomous Unmanned Vehicles (AUVs) are systems that are capable of maneuvering in the air, on the ground, above or under the water. There are a number of potential applications for AUVs in civilian, military, and security areas, for example, defense patrol duties, agricultural activities, forest fire monitoring and control, grid monitoring, boarder control, search, surveillance, and rescue. AUVs have great potential benefits to Canada for numerous reasons; many of which are connected with our large uninhabited land, the largest area of forests in the world, and the longest international border in the world.
This project is to develop control and navigation algorithms for autonomous unmanned vehicles (drones, mobile robots, and self-driving cars) for indoor surveillance and monitoring applications.
Duties and Responsibilities: The successful students will be working with graduate students and research fellows on (a) programming; (b) hardware development and implementation. Through these activities, the students will gain experience in control and navigation system design, hardware, and software development, etc. These experiences will be very helpful for the student’s future study and work.
Desired Technical Skills: Good programming skills, MATLAB, C, and Linux, enrolled in an engineering degree, familiar with ROS, and a team player.
Desired Course(s): Senior engineering students.
Other Desired Qualifications: None.
Professor: Regina Lee
Contact Info: reginal@yorku.ca
Lab Website: https://nanosatellite.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA), NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: This project involves working with Resident Space Object (RSO) light curves, which are time series brightness data of satellites. The first part of the project involves labelling existing light curves by the type of object, such as a satellite, rocket body or debris. Following the research assistant’s understanding of light curves and how different light curves look like, light curve extraction and analysis will be explored. As part of the STRATOS 2023 mission, our team launched a payload on a stratospheric balloon, named RSONAR II, which captured starfield images. The overarching goal of the project is to extract light curves of RSOs from the images and use those light curves to understand key factors about an RSOs shape and motion.
Duties and Responsibilities: The RA will be responsible for labelling light curves using online information about the status of an RSO. Following that, the RA will develop an algorithm to extract light curves from ground-based images.
Desired Technical Skills: Programming (Python and MATLAB preferred. Some of the helpful skills for this project include image processing. Familiarity with light curves and astronomical imaging tools such as AstroImageJ are also assets.
Desired Course(s): Space, Computer, or Software Engineering.
Other Desired Qualifications: N/A.
Contact Info: reginal@yorku.ca
Lab Website: https://nanosatellite.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA), NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: This project involves working with Resident Space Object (RSO) light curves, which are time series brightness data of satellites. The first part of the project involves labelling existing light curves by the type of object, such as a satellite, rocket body or debris. Following the research assistant’s understanding of light curves and how different light curves look like, light curve extraction and analysis will be explored. As part of the STRATOS 2023 mission, our team launched a payload on a stratospheric balloon, named RSONAR II, which captured starfield images. The overarching goal of the project is to extract light curves of RSOs from the images and use those light curves to understand key factors about an RSOs shape and motion.
Duties and Responsibilities: The RA will be responsible for labelling light curves using online information about the status of an RSO. Following that, the RA will develop an algorithm to extract light curves from ground-based images.
Desired Technical Skills: Programming (Python and MATLAB preferred. Some of the helpful skills for this project include image processing. Familiarity with light curves and astronomical imaging tools such as AstroImageJ are also assets.
Desired Course(s): Space, Computer, or Software Engineering.
Other Desired Qualifications: N/A.
Professor: Baoxin Hu
Contact Info: baoxin@yorku.ca
Lab Website: https://eo.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: Terrestrial and arboreal lichens are critical winter forage for woodland caribou and have a great impact on water and nutrient cycles and forest ecosystem dynamics. However, the distribution of lichen cover is not well known, and the drivers of lichen cover dynamics are poorly understood. Mapping lichens is crucial for social license in sustainable forest management and biodiversity conservation. This project aims to exploit single photon LiDAR (SPL) data to map lichens in the Ontario boreal forest.
Duties and Responsibilities: Interpret the photos collected in various test plots for lichen cover. Develop a classification scheme to categorize cover. Aid the processing and analysis of LiDAR data.
Desired Technical Skills: Skills in data analysis, image processing, programming.
Desired Course(s): Computer science, Geomatics, Physical geography, Mathematics.
Other Desired Qualifications: Good written and oral communication skills.
Contact Info: baoxin@yorku.ca
Lab Website: https://eo.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: Terrestrial and arboreal lichens are critical winter forage for woodland caribou and have a great impact on water and nutrient cycles and forest ecosystem dynamics. However, the distribution of lichen cover is not well known, and the drivers of lichen cover dynamics are poorly understood. Mapping lichens is crucial for social license in sustainable forest management and biodiversity conservation. This project aims to exploit single photon LiDAR (SPL) data to map lichens in the Ontario boreal forest.
Duties and Responsibilities: Interpret the photos collected in various test plots for lichen cover. Develop a classification scheme to categorize cover. Aid the processing and analysis of LiDAR data.
Desired Technical Skills: Skills in data analysis, image processing, programming.
Desired Course(s): Computer science, Geomatics, Physical geography, Mathematics.
Other Desired Qualifications: Good written and oral communication skills.
Professor: Michael C.F. Bazzocchi
Contact Info: mbazz@yorku.ca
Lab Website: https://www.astrolabresearch.com/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Currently, the Astronautics and Robotics Laboratory (ASTRO Lab) is seeking students for research projects in spacecraft dynamics and control emulation using robotic testbeds under the supervision of Prof. Michael Bazzocchi. Students will be engaged in projects related to robotic simulation, experimental emulation, and fabrication of systems related to spacecraft dynamics and control, including interaction dynamics for tasks such as space debris capture and removal. For more information about the lab and associated projects, please refer to https://www.astrolabresearch.com/
Duties and Responsibilities: Specific student project details are determined based on the students’ expertise as well as their research and professional goals. Generally, students are expected to formulate clear research objectives, perform a literature review to gain a working understanding in their research areas, construct a clear methodology and/or test procedure for their research and experiments, conduct simulations, fabricate prototypes, and conduct experiments, and document their research in formal technical reports or manuscripts for publication.
Desired Technical Skills: Students with some experience in the following areas are especially encouraged to apply: orbital mechanics, spacecraft dynamics and control, robotics. Applicants should have proficiency in technical writing and experience in computer programming, CAD experience is an asset.
Desired Course(s): Space or Robotics Engineering (or similar).
Other Desired Qualifications: Applicants with interests in long-term research projects or in other projects within the ASTRO Lab research areas are also encouraged to apply.
Contact Info: mbazz@yorku.ca
Lab Website: https://www.astrolabresearch.com/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Currently, the Astronautics and Robotics Laboratory (ASTRO Lab) is seeking students for research projects in spacecraft dynamics and control emulation using robotic testbeds under the supervision of Prof. Michael Bazzocchi. Students will be engaged in projects related to robotic simulation, experimental emulation, and fabrication of systems related to spacecraft dynamics and control, including interaction dynamics for tasks such as space debris capture and removal. For more information about the lab and associated projects, please refer to https://www.astrolabresearch.com/
Duties and Responsibilities: Specific student project details are determined based on the students’ expertise as well as their research and professional goals. Generally, students are expected to formulate clear research objectives, perform a literature review to gain a working understanding in their research areas, construct a clear methodology and/or test procedure for their research and experiments, conduct simulations, fabricate prototypes, and conduct experiments, and document their research in formal technical reports or manuscripts for publication.
Desired Technical Skills: Students with some experience in the following areas are especially encouraged to apply: orbital mechanics, spacecraft dynamics and control, robotics. Applicants should have proficiency in technical writing and experience in computer programming, CAD experience is an asset.
Desired Course(s): Space or Robotics Engineering (or similar).
Other Desired Qualifications: Applicants with interests in long-term research projects or in other projects within the ASTRO Lab research areas are also encouraged to apply.
Improvement of Smartphone Positioning
Professor: Sunil Bisnath
Contact Info: sbisnath@yorku.ca
Lab Website: https://gnsslab.lassonde.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Low-cost GPS, and now broadly GNSS (Global Navigation Satellite System), chips in smartphones are increasing in capabilities. Coupled with users being able to access raw measurements from some models, the York GNSS Lab has been applying high-performance measurement processing techniques to improve positioning accuracy from 10s of metres to the decimetre level. This solution can also be integrated with measurements from other smartphone sensors, such as Inertial Measurement Unit (IMU). Applications for such performance include navigation, augmented reality apps, gaming, etc. Research areas of interest include: precision of raw satellite ranging measurements, GNSS/IMU integration, optimal estimation, performance of cellphone antennas, availability of measurements in obstructed, urban environments, tuning of processing methods for such measurements, application of machine learning approaches to mitigate measurement errors, and solution testing.
Duties and Responsibilities: Working with a team of PDF and PhD students in the collection, analysis, and processing of smartphone GNSS data, and the tuning of GNSS measurement processing algorithms. This work is globally leading-edge, so there is the high likelihood of conference and journal paper preparation experience as well.
Desired Technical Skills: Knowledge of GNSS specifically, optimal estimation, and Geomatics in general, would be very helpful, but not absolutely required. As well as the scientific method, machine learning, and data analysis skills. Coding ability in, e.g., MatLab, Python, C/C++ will be very helpful.
Desired Course(s): Current BEng or BSc student in Geomatics, Space, Computer, Electrical, Computer Science or a related field. Having taken LE/ESSE 3670 3.00 – Global Navigation Satellite Systems or equivalent would be very helpful, but not necessary. Strong math and coding backgrounds are significant assets.
Other Desired Qualifications: A quick learner. Focused and organized. Strong communications skills. Proven ability to work in a group and individually. Highly motivated. Can work independently. Interested in graduate school.
Professor: Sunil Bisnath
Contact Info: sbisnath@yorku.ca
Lab Website: https://gnsslab.lassonde.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Low-cost GPS, and now broadly GNSS (Global Navigation Satellite System), chips in smartphones are increasing in capabilities. Coupled with users being able to access raw measurements from some models, the York GNSS Lab has been applying high-performance measurement processing techniques to improve positioning accuracy from 10s of metres to the decimetre level. This solution can also be integrated with measurements from other smartphone sensors, such as Inertial Measurement Unit (IMU). Applications for such performance include navigation, augmented reality apps, gaming, etc. Research areas of interest include: precision of raw satellite ranging measurements, GNSS/IMU integration, optimal estimation, performance of cellphone antennas, availability of measurements in obstructed, urban environments, tuning of processing methods for such measurements, application of machine learning approaches to mitigate measurement errors, and solution testing.
Duties and Responsibilities: Working with a team of PDF and PhD students in the collection, analysis, and processing of smartphone GNSS data, and the tuning of GNSS measurement processing algorithms. This work is globally leading-edge, so there is the high likelihood of conference and journal paper preparation experience as well.
Desired Technical Skills: Knowledge of GNSS specifically, optimal estimation, and Geomatics in general, would be very helpful, but not absolutely required. As well as the scientific method, machine learning, and data analysis skills. Coding ability in, e.g., MatLab, Python, C/C++ will be very helpful.
Desired Course(s): Current BEng or BSc student in Geomatics, Space, Computer, Electrical, Computer Science or a related field. Having taken LE/ESSE 3670 3.00 – Global Navigation Satellite Systems or equivalent would be very helpful, but not necessary. Strong math and coding backgrounds are significant assets.
Other Desired Qualifications: A quick learner. Focused and organized. Strong communications skills. Proven ability to work in a group and individually. Highly motivated. Can work independently. Interested in graduate school.
Professor: Gunho Sohn
Contact Info: gsohn@yorku.ca
Lab Website: https://gunhosohn.me/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: This project is centered on advancing a drone-based positioning and navigation system, specifically designed for critical applications such as construction site monitoring and bridge inspection. Developed within the Augmented Urban Space Modeling (AUSM) lab, our Quality-aware Drone (Q-Drone) system integrates state-of-the-art visual and LiDAR sensors, UWB-based (Ultra-Wideband) positioning, and an AI-driven navigation system. The objective is to enhance this system by implementing an AI-based online positioning framework coupled with a sophisticated path-planning algorithm. This enhancement aims to achieve a dual goal: ensuring safe navigation by optimizing collision avoidance strategies and enhancing the quality of data acquisition in complex three-dimensional spaces. The project is a forward-thinking endeavor to elevate the capabilities of drone technology in urban space modeling and infrastructure inspection, leveraging AI to navigate and analyze with unprecedented precision and efficiency.
Duties and Responsibilities: The research interns will engage in a multifaceted and collaborative environment, working closely with graduate students, post-doctoral fellows, and partners at the University of Sherbrooke. Their responsibilities will be integral to advancing the capabilities of the Q-Drone system: 1. Sensor Calibration and Integration: Conduct meticulous calibration of the Q-Drone’s visual and LiDAR sensors, along with positioning and orientation systems, ensuring data accuracy and system dependability. 2. Algorithm Optimization: Enhance the lab’s SLAM and graph pose backend algorithms for real-time efficiency, focusing on improving computational speed, accuracy, and environmental adaptability. 3. AI Network Enhancement: Develop and refine AI models for detecting structural anomalies (cracks, spalling, etc.) in bridge inspections, optimizing for speed, accuracy, and real-time applicability using the Q-Drone’s data.
These duties are not only critical for the project’s success but also offer the interns a unique opportunity to gain hands-on experience in advanced drone technology, AI applications, and collaborative research dynamics. The interns are expected to maintain rigorous documentation, engage in active problem-solving, and contribute to the dissemination of research findings, all while working in a cutting-edge technological environment.
Desired Technical Skills: The ideal candidate for this research internship will possess a robust set of technical skills tailored to the demands of advanced drone navigation and AI-driven analysis: 1. Robot Operating System (ROS) Proficiency: Demonstrable experience in utilizing ROS for real-time robotics applications. Familiarity with ROS tools and libraries is essential, enabling seamless integration and manipulation of complex robotic systems. 2. Expertise in SLAM and Real-time Computing: A strong background in Simultaneous Localization and Mapping (SLAM) techniques, with the ability to optimize and implement these algorithms for real-time operations. Proficiency in managing computational resources effectively to ensure timely data processing and system responsiveness. 3. Robotics and Computer Vision Programming: Practical experience in developing and deploying robotic systems, with a particular focus on computer vision. The ability to program and troubleshoot algorithms that enable machines to navigate and understand their environment is crucial. 4. Deep Learning and AI Application Development: Solid programming experience in deep learning, with a track record of applying these skills to solve real-world problems. Proficiency in deep learning frameworks (such as TensorFlow or PyTorch) is necessary, along with the ability to develop, train, and optimize neural networks, particularly for tasks related to anomaly detection and image analysis.
The candidate should have a passion for technology and innovation, with a commitment to applying their programming skills in robotics, computer vision, and deep learning to drive advancements in drone navigation and structural analysis. They should be eager to work collaboratively, solve complex problems, and contribute to cutting-edge research in the field.
Desired Course(s): Candidates interested in this research internship should ideally be enrolled in or have completed degree programs or courses closely related to the following disciplines, ensuring a strong foundational knowledge and skill set pertinent to the project’s technical demands: 1. Systems Engineering: Specialization or coursework in Systems Engineering, focusing on the integration of complex systems and the interdisciplinary approach to building efficient, reliable systems – a key aspect of drone technology and navigation. 2. Engineering Disciplines with a Focus on Software, Computer, or Mechatronics: Degree programs or substantial coursework in Software Engineering, Computer Engineering, or Mechatronics Engineering. 3. Edge Computing: Specialized knowledge or coursework in Edge Computing. 4. Computer Vision, Robotics, and Artificial Intelligence: Academic background or extensive coursework in Computer Vision and Robotics, equipping candidates with the skills to develop systems that can perceive, analyze, and interpret visual information.
Other Desired Qualifications: 1. Research and Analytical Skills: Experience in conducting research, with the ability to design experiments, collect and analyze data, and draw meaningful conclusions. Proficiency in using analytical software and tools is highly beneficial. 2. Problem-Solving and Critical Thinking: Demonstrated ability to approach complex problems systematically, think critically, and devise innovative solutions. 3. Communication and Collaboration: Strong verbal and written communication skills, with the capability to document work clearly and present findings effectively. The candidate should also be adept at collaborative work and able to contribute positively in a team setting. 4. Initiative and Self-Motivation: A proactive attitude, with a willingness to take initiative and work independently when necessary. The ideal candidate should be eager to learn, adapt, and take on challenges with minimal supervision. 5. Project Management: Basic understanding of project management principles, with the ability to manage time effectively, meet deadlines, and coordinate tasks within a multi-disciplinary team.
Contact Info: gsohn@yorku.ca
Lab Website: https://gunhosohn.me/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: This project is centered on advancing a drone-based positioning and navigation system, specifically designed for critical applications such as construction site monitoring and bridge inspection. Developed within the Augmented Urban Space Modeling (AUSM) lab, our Quality-aware Drone (Q-Drone) system integrates state-of-the-art visual and LiDAR sensors, UWB-based (Ultra-Wideband) positioning, and an AI-driven navigation system. The objective is to enhance this system by implementing an AI-based online positioning framework coupled with a sophisticated path-planning algorithm. This enhancement aims to achieve a dual goal: ensuring safe navigation by optimizing collision avoidance strategies and enhancing the quality of data acquisition in complex three-dimensional spaces. The project is a forward-thinking endeavor to elevate the capabilities of drone technology in urban space modeling and infrastructure inspection, leveraging AI to navigate and analyze with unprecedented precision and efficiency.
Duties and Responsibilities: The research interns will engage in a multifaceted and collaborative environment, working closely with graduate students, post-doctoral fellows, and partners at the University of Sherbrooke. Their responsibilities will be integral to advancing the capabilities of the Q-Drone system: 1. Sensor Calibration and Integration: Conduct meticulous calibration of the Q-Drone’s visual and LiDAR sensors, along with positioning and orientation systems, ensuring data accuracy and system dependability. 2. Algorithm Optimization: Enhance the lab’s SLAM and graph pose backend algorithms for real-time efficiency, focusing on improving computational speed, accuracy, and environmental adaptability. 3. AI Network Enhancement: Develop and refine AI models for detecting structural anomalies (cracks, spalling, etc.) in bridge inspections, optimizing for speed, accuracy, and real-time applicability using the Q-Drone’s data.
These duties are not only critical for the project’s success but also offer the interns a unique opportunity to gain hands-on experience in advanced drone technology, AI applications, and collaborative research dynamics. The interns are expected to maintain rigorous documentation, engage in active problem-solving, and contribute to the dissemination of research findings, all while working in a cutting-edge technological environment.
Desired Technical Skills: The ideal candidate for this research internship will possess a robust set of technical skills tailored to the demands of advanced drone navigation and AI-driven analysis: 1. Robot Operating System (ROS) Proficiency: Demonstrable experience in utilizing ROS for real-time robotics applications. Familiarity with ROS tools and libraries is essential, enabling seamless integration and manipulation of complex robotic systems. 2. Expertise in SLAM and Real-time Computing: A strong background in Simultaneous Localization and Mapping (SLAM) techniques, with the ability to optimize and implement these algorithms for real-time operations. Proficiency in managing computational resources effectively to ensure timely data processing and system responsiveness. 3. Robotics and Computer Vision Programming: Practical experience in developing and deploying robotic systems, with a particular focus on computer vision. The ability to program and troubleshoot algorithms that enable machines to navigate and understand their environment is crucial. 4. Deep Learning and AI Application Development: Solid programming experience in deep learning, with a track record of applying these skills to solve real-world problems. Proficiency in deep learning frameworks (such as TensorFlow or PyTorch) is necessary, along with the ability to develop, train, and optimize neural networks, particularly for tasks related to anomaly detection and image analysis.
The candidate should have a passion for technology and innovation, with a commitment to applying their programming skills in robotics, computer vision, and deep learning to drive advancements in drone navigation and structural analysis. They should be eager to work collaboratively, solve complex problems, and contribute to cutting-edge research in the field.
Desired Course(s): Candidates interested in this research internship should ideally be enrolled in or have completed degree programs or courses closely related to the following disciplines, ensuring a strong foundational knowledge and skill set pertinent to the project’s technical demands: 1. Systems Engineering: Specialization or coursework in Systems Engineering, focusing on the integration of complex systems and the interdisciplinary approach to building efficient, reliable systems – a key aspect of drone technology and navigation. 2. Engineering Disciplines with a Focus on Software, Computer, or Mechatronics: Degree programs or substantial coursework in Software Engineering, Computer Engineering, or Mechatronics Engineering. 3. Edge Computing: Specialized knowledge or coursework in Edge Computing. 4. Computer Vision, Robotics, and Artificial Intelligence: Academic background or extensive coursework in Computer Vision and Robotics, equipping candidates with the skills to develop systems that can perceive, analyze, and interpret visual information.
Other Desired Qualifications: 1. Research and Analytical Skills: Experience in conducting research, with the ability to design experiments, collect and analyze data, and draw meaningful conclusions. Proficiency in using analytical software and tools is highly beneficial. 2. Problem-Solving and Critical Thinking: Demonstrated ability to approach complex problems systematically, think critically, and devise innovative solutions. 3. Communication and Collaboration: Strong verbal and written communication skills, with the capability to document work clearly and present findings effectively. The candidate should also be adept at collaborative work and able to contribute positively in a team setting. 4. Initiative and Self-Motivation: A proactive attitude, with a willingness to take initiative and work independently when necessary. The ideal candidate should be eager to learn, adapt, and take on challenges with minimal supervision. 5. Project Management: Basic understanding of project management principles, with the ability to manage time effectively, meet deadlines, and coordinate tasks within a multi-disciplinary team.
Professor: Gunho Sohn
Contact Info: gsohn@yorku.ca
Lab Website: https://gunhosohn.me/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: This project is at the forefront of Metaverse technology, focusing on enhancing pedestrian safety and comfort through the simulation of Sidewalk Delivery Robots (SDRs) in virtual and augmented reality environments. Utilizing the advanced York University Campus Digital Twin, along with UNITY and Esri GIS systems, the intern will develop and refine AR experiences, integrating them into our existing SDR simulations. This immersive approach aims to create a comprehensive Metaverse experience, allowing for intricate analysis and optimization of SDR operations and their interaction with pedestrians, thereby ensuring a harmonious integration of autonomous delivery robots into urban life.
Duties and Responsibilities: The intern will: Utilize the York University Campus Digital Twin as a base for simulating realistic urban scenarios and SDR interactions with pedestrians. Work within the UNITY and Esri GIS systems to enhance and further develop SDR simulations, ensuring high fidelity and accurate representation of real-world dynamics. Collaborate closely with industrial partners, gaining insights from the industry perspective and integrating practical considerations into the simulation and algorithm refinement processes. Design and develop AR components, integrating them with existing VR simulations to enhance the realism and interactive capabilities of the SDR simulations. Explore innovative AR/VR interaction techniques to simulate and analyze pedestrian-SDR dynamics effectively. Conduct usability testing of the AR/VR simulations, gathering and analyzing user feedback to continually refine the user experience within the Metaverse framework.
Desired Technical Skills: Experience or familiarity with UNITY and Esri GIS systems for virtual environment development and spatial analysis. Ability to work with digital twin technologies and understand their application in simulating real-world scenarios. Strong skills in AR and VR development, with experience in UNITY, Esri GIS systems, or similar platforms. Knowledge of or experience in creating immersive and interactive user experiences in the Metaverse. Proficiency in developing AR applications, with a focus on user interaction and experience design.
Desired Course(s): For this immersive and technologically advanced project, candidates should be enrolled in or have completed degree programs or courses that provide a strong foundation in virtual and augmented reality, computer science, and interactive system design. Specifically, the desired academic backgrounds include: 1. Computer Science or Information Technology: Specialization or significant coursework in software development, especially focusing on virtual and augmented reality. Candidates should have a strong grasp of algorithm design, system architecture, and user-centered design principles. 2. Interactive Media or Game Design: Academic training in interactive media or game design, with a focus on creating immersive and engaging user experiences. Proficiency in design tools and software commonly used in the industry, such as Unity or Unreal Engine, is highly advantageous. 3. Electrical or Computer Engineering: Degree programs that emphasize the hardware-software interface, sensor technologies, and networked systems. 4. Graphic Design with a focus on 3D Modeling: Coursework in graphic design, particularly focusing on 3D modeling and animation. Understanding the principles of visual aesthetics and being able to create realistic, high-quality 3D assets is essential for developing compelling AR/VR environments. 5. Human-Computer Interaction (HCI) or User Experience (UX) Design: Specialization in HCI or UX design, focusing on the study of how people interact with computers and technology.
Other Desired Qualifications: 1. Research and Analytical Skills: Experience in conducting research, with the ability to design experiments, collect and analyze data, and draw meaningful conclusions. Proficiency in using analytical software and tools is highly beneficial. 2. Problem-Solving and Critical Thinking: Demonstrated ability to approach complex problems systematically, think critically, and devise innovative solutions. 3. Communication and Collaboration: Strong verbal and written communication skills, with the capability to document work clearly and present findings effectively. The candidate should also be adept at collaborative work and able to contribute positively in a team setting. 4. Initiative and Self-Motivation: A proactive attitude, with a willingness to take initiative and work independently when necessary. The ideal candidate should be eager to learn, adapt, and take on challenges with minimal supervision. 5. Project Management: Basic understanding of project management principles, with the ability to manage time effectively, meet deadlines, and coordinate tasks within a multi-disciplinary team.
Contact Info: gsohn@yorku.ca
Lab Website: https://gunhosohn.me/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: This project is at the forefront of Metaverse technology, focusing on enhancing pedestrian safety and comfort through the simulation of Sidewalk Delivery Robots (SDRs) in virtual and augmented reality environments. Utilizing the advanced York University Campus Digital Twin, along with UNITY and Esri GIS systems, the intern will develop and refine AR experiences, integrating them into our existing SDR simulations. This immersive approach aims to create a comprehensive Metaverse experience, allowing for intricate analysis and optimization of SDR operations and their interaction with pedestrians, thereby ensuring a harmonious integration of autonomous delivery robots into urban life.
Duties and Responsibilities: The intern will: Utilize the York University Campus Digital Twin as a base for simulating realistic urban scenarios and SDR interactions with pedestrians. Work within the UNITY and Esri GIS systems to enhance and further develop SDR simulations, ensuring high fidelity and accurate representation of real-world dynamics. Collaborate closely with industrial partners, gaining insights from the industry perspective and integrating practical considerations into the simulation and algorithm refinement processes. Design and develop AR components, integrating them with existing VR simulations to enhance the realism and interactive capabilities of the SDR simulations. Explore innovative AR/VR interaction techniques to simulate and analyze pedestrian-SDR dynamics effectively. Conduct usability testing of the AR/VR simulations, gathering and analyzing user feedback to continually refine the user experience within the Metaverse framework.
Desired Technical Skills: Experience or familiarity with UNITY and Esri GIS systems for virtual environment development and spatial analysis. Ability to work with digital twin technologies and understand their application in simulating real-world scenarios. Strong skills in AR and VR development, with experience in UNITY, Esri GIS systems, or similar platforms. Knowledge of or experience in creating immersive and interactive user experiences in the Metaverse. Proficiency in developing AR applications, with a focus on user interaction and experience design.
Desired Course(s): For this immersive and technologically advanced project, candidates should be enrolled in or have completed degree programs or courses that provide a strong foundation in virtual and augmented reality, computer science, and interactive system design. Specifically, the desired academic backgrounds include: 1. Computer Science or Information Technology: Specialization or significant coursework in software development, especially focusing on virtual and augmented reality. Candidates should have a strong grasp of algorithm design, system architecture, and user-centered design principles. 2. Interactive Media or Game Design: Academic training in interactive media or game design, with a focus on creating immersive and engaging user experiences. Proficiency in design tools and software commonly used in the industry, such as Unity or Unreal Engine, is highly advantageous. 3. Electrical or Computer Engineering: Degree programs that emphasize the hardware-software interface, sensor technologies, and networked systems. 4. Graphic Design with a focus on 3D Modeling: Coursework in graphic design, particularly focusing on 3D modeling and animation. Understanding the principles of visual aesthetics and being able to create realistic, high-quality 3D assets is essential for developing compelling AR/VR environments. 5. Human-Computer Interaction (HCI) or User Experience (UX) Design: Specialization in HCI or UX design, focusing on the study of how people interact with computers and technology.
Other Desired Qualifications: 1. Research and Analytical Skills: Experience in conducting research, with the ability to design experiments, collect and analyze data, and draw meaningful conclusions. Proficiency in using analytical software and tools is highly beneficial. 2. Problem-Solving and Critical Thinking: Demonstrated ability to approach complex problems systematically, think critically, and devise innovative solutions. 3. Communication and Collaboration: Strong verbal and written communication skills, with the capability to document work clearly and present findings effectively. The candidate should also be adept at collaborative work and able to contribute positively in a team setting. 4. Initiative and Self-Motivation: A proactive attitude, with a willingness to take initiative and work independently when necessary. The ideal candidate should be eager to learn, adapt, and take on challenges with minimal supervision. 5. Project Management: Basic understanding of project management principles, with the ability to manage time effectively, meet deadlines, and coordinate tasks within a multi-disciplinary team.