【博士奖学金】最新PhD招生和奖学金信息（209）

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The Aggregation of Digital Twin Technology and Multi-sensor Fusion in Highly Automated Vehicles

University of Leeds：Faculty of Environment

Supervisor：Dr Mahdi Rezaei, Prof NM Merat

Application Deadline ：Friday, February 25, 2022

Competition Funded PhD Project (Students Worldwide)

About the Project

Highly automated vehicles known as Level 4 vehicles, can operate in self-driving mode in most circumstances; and the driver is allowed to involve in various types of non-driving activities, including sleeping. However, the automated driving mode is only expected to perform in limited geofenced areas called the operational design domain (ODD). Therefore, a human driver still has to be present with the option of resuming the driving control whenever required.

Utilising computer vision, machine learning, and the aggregation of multi-sensor data fusion with the hot concept of the digital twin technology, this research focuses on enhancing the level of vehicle automation, extending the ODD, increasing the comfort of the driver and passengers, and promoting the public trust in autonomous vehicles (AVs).

Using camera and LiDAR point cloud data, the research percepts the traffic and road environment condition, as well as assessing the driver readiness for a safe and swift response to a takeover request (TOR). The research involves driver behaviour monitoring (eye, head pose, hand, and body pose modelling), traffic perception and understanding (vehicle, pedestrian and cyclist detection), and tracking.

Background and objectives

Institute for Transport Studies (ITS) at the University of Leeds (one of the World’s Top 100 Universities) is the leading centre in Autonomous Vehicles research in the United Kingdom and one of the top 10 global pioneers in the field. The centre currently has 80+ senior academic staff, researchers, and PhD students in multi-disciplinary areas of computer vision, AI and machine learning, engineering, behaviour modelling, human factors, psychology, and transportation safety.

The research performs studies in both driver behaviour monitoring and 360° environment perception using multiple sensors such as cameras, LiDAR, RADAR, and communication with digital twin information/infrastructures for safer and swift transition between human driving mode and autonomous mode, whenever required. The in-cabin situation awareness research involves head pose, eye gaze, and body pose monitoring and modelling, and the environment perception involves understanding the road/traffic condition, including road users detection (vehicles, pedestrians, cyclists), tracking, and trajectory estimation. Although the student is encouraged to work on both in-cabin and road understanding and monitoring, the main focus would be on traffic environment perception. While we put more focus on computer vision and machine learning-based research; the PhD candidate has the option of performing joint research within our great interdisciplinary team with various backgrounds, from the human factor to mathematics, robotics, and civil engineering.

Essential Qualifications and Skills

• A master’s degree in a relevant discipline (e.g. Computer Science, Artificial Intelligence, Data Analytics, Machine Learning, Robotics, or Mathematics) with a minimum grade of Merit or equivalent degree from an overseas university.
  
• In-depth understanding and practical experience in digital image/video processing, computer vision, machine learning, linear algebra, and statistics
• Programming proficiency (e.g. in Python, C++, or R)
• Submission of a research proposal for the PhD topic, including introduction, related works, research gaps, aims and objectives, methodology, outcomes, timetable, and references.  

Desired Qualifications

• A track record of previous publication(s) in the field of computer vision and digital image/video processing in well-known conference venues/journals 

• Graduation with a 1st class bachelor and a distinction (A/A+) masters degree

• Practical experience with LiDAR point cloud and multi-sensor data fusion/aggregations solutions

Entry requirements

Applicants to research degree programmes should normally have at least a first-class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. 

English language requirements

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have higher requirements.

How to apply

Formal applications for research degree study should be made online through the University's website. Please state clearly in the research information section that the research degree you wish to be considered for is Aggregation of Digital Twin Technology and Multi-sensor Fusion Towards Highly Automated Vehicles as well as Dr Mahdi Rezaei as your proposed supervisor.

The programme code that you need to apply for is PHP-ENVE-FT which is EPSRC DTP Environment.

If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements.

Funding Notes

This 3.5-year EPSRC DTP award will provide full tuition fees, a stipend at the UK research council rate (UK Sterling £15,840 for 2022/23), and a research training and support grant.

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3

Antimicrobial peptide double cooperativity towards new antimicrobial agents

The University of Tokyo: Institute of Industrial Science

Supervisor：Prof K Sugihara

Application Deadline ：Sunday, January 30, 2022

Competition Funded PhD Project (Students Worldwide)

About the Project

A mixture of different types of biomolecules sometimes boosts or suppresses their activities or even generates a new function known as cooperativity. Recently we have reported a unique cooperative function between two well-known antimicrobial peptides (LL-37/HNP1) that kills bacteria more efficiently while minimizing the host damage by suppressing mammalian cell membrane lysis. Such a “double cooperativity” may be used in our immune system and may help with developing efficient and safe antimicrobial agents for the current crisis of antimicrobial resistance.

In this project, we will challenge mammalian cells and mammalian cell membrane mimics by LL-37, HNP1, and their mixture, and will study their responses by biophysical assays based on supported and pore-spanning bilayers, such as fluorescence recovery after photobleaching (FRAP), quartz crystal microbalance with dissipation (QCM-D), electrochemical impedance spectroscopy (EIS), and single-channel conductance measurement.

Qualifications

Highly-motivated students with a background in physics, chemistry, biophysics, biochemistry, biomedical engineering who are interested in pursuing a research career are encouraged to apply.

How to apply

1. If you are interested, please send your CV to [e-mail]. Selected candidates will go through an online Interview. If the lab agreed to host you, you will move on to the following step 2.

2. At the University of Tokyo, foreign students need to apply for an IME graduate program (http://www.ime.t.u-tokyo.ac.jp/prospectus-2022.html), while specifying the lab as a host with the lab’s approval. Candidates have to pass the selection of these programs to be enrolled at the University of Tokyo. Although it is competitive-based, there is a fair chance to obtain funding.

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Dieback of Stringybark Forests in the Mount Lofty Ranges - extent and causes

University of South Australia：PhD Opportunities

Supervisor：Assoc Prof Gunnar Keppel, Dr Stefan Peters

Funded PhD Project (Students Worldwide)

About the Project

Increasing drought and heatwave conditions linked to climate change are negatively impacting stringybark forests, resulting in forest canopy dieback. Stringybark forests are iconic in South Australia, are important wildlife habitats, and store considerable amounts of carbon. This project will investigate the extent, causes and flow-on environmental and ecological impacts of stringybark dieback in the Mount Lofty Ranges using field surveys and/or remote sensing.

Determining the flow-on effects of dieback on species composition, vegetation structure and microclimate in these forests will also help us identify ecosystem-level impacts of dieback. Therefore, the project will develop a holistic picture of the extent, causes, consequences and conservation of stringybark forests in the Mount Lofty Ranges.

This project will allow you to apply ecological and remote sensing skills to a key conservation problem in South Australia with global applications. You’ll also benefit from exposure to the multi-institutional project team, involving stakeholders in government, local council, environmental groups and other universities. As part of the Biodiversity Group you will also be encouraged to participate in and contribute to the International Tree Mortality Network, which is part of the International Union of Forest Research Organizations (IUFRO).This project collaborates with and is informed by the Australian government, local councils and NGOs. 

What you’ll do

In this research degree project, you will undertake data collection using field surveys and remote sensing. The field surveys will include assessing the health status of trees and the impact of canopy loss on microclimate. Remote sensing will involve integrating existing data from various sources to quantify the extent of canopy dieback. Collected data will be processed and analysed using appropriate methods.

In this partnered project you will present your research outputs to stakeholders and be encouraged to publish in leading ecological and conservation journals.

This is an exceptional research project to address our current climate crisis and will position you as an expert in your field with valuable research and industry experience.

Eligibility and Selection

This project is open to application from both domestic and international applicants.

Applicants must meet the eligibility criteria for entrance into a PhD. Additionally applicants must meet the projects selection criteria:

Essential

• knowledge of ecological principles and analyses

• knowledge of basic GIS and remote sensing skills

Desirable

• ability to undertake statistical analyses in the R statistical environment

All applications that meet the eligibility and selection criteria will be considered for this project. A merit selection process will be used to determine the successful candidate.

The successful applicant is expected to study full-time, and to be based at our Mawson Lakes Campus in the north of Adelaide. Note that international students on a student visa will need to study full-time.

Funding Notes

This project is funded for reasonable research expenses. Additionally, a living allowance scholarship of $28,854 per annum is available to eligible applicants. A fee-offset or waiver for the standard term of the program is also included. 

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