[robotics-worldwide] [jobs]: PhD Studentship - Training Self-Driving Cars using Deep Reinforcement Learning

Faria, Diego d.faria at aston.ac.uk
Tue Jul 30 08:00:22 PDT 2019


PhD Studentship: Training Self-Driving Cars using Deep Reinforcement Learning

Location:  Aston University - Main Campus - Birmingham, UK
Basis:  Full Time
Closing Date: 23.59 hours BST on Friday 30 August 2019
Reference: R190303
PhD Studentship (3.5 years)

Supervisor(s): Dr Diego R. Faria (main) and Dr Maria Chli (associate)

Key words: Connected Autonomous Vehicles, Vehicle to Everything (V2X), Computer Vision, Deep Learning, Advanced Driving Assistance Systems.

Applications are invited for a studentship in the Aston Institute of Urban Technology and the Environment (ASTUTE), funded by the School of Engineering and Applied Science. The successful applicant will join a cohort of graduate students working on projects across the broader Smart Cities field, and as part of the PhD will receive training and experience in collaborative research, relevant to industry and Smart City planners. The studentship is offered in collaboration with Ranplan Wireless, Huawei and Birmingham City Council.

The position is available to start in October 2019

Financial Support
Full for EU/Home Students: This studentship includes a fee bursary to cover the home/EU fees rate, plus a maintenance allowance of £15,009 in 2019/20 (subject to eligibility).

Overseas Applicants
Applicants from outside the EU may apply for this studentship, BUT will need to pay the difference between the ‘Home/EU’ and the ‘Overseas’ tuition fees, currently this is {£12,573 in 2019/20}.  As part of the application you will be required to confirm that you have access to this additional funding. Maintenance allowance is similar to EU/Home students.

Project Description
The pathway to make autonomous driving a truly ubiquitous technology is to ensure functional safety, at the same time as ability to effectively respond to unexpected events. This project puts forward the development of an autonomous system with capabilities to navigate in the absence of maps and explicit rules, relying - similarly to humans - on a comprehensive understanding of the environment, while following simple higher-level directions. It is expected that the Ph.D. candidate will design and develop an approach using the local scene captured from images and deep reinforcement learning for autonomous driving with a human in the loop. In addition, knowledge transfer will be exploited from simulated environment to the real-world application to boost learning performance. A staged approach will be adopted as follows: (i) utilising a simulated environment for training/testing to enhance the problem understanding, and to enable the design of suitable model architectures and hyperparameter selection for lane following task; then (ii) transferring the knowledge from the simulated task, and continuation of training in a real environment;  for finally (iii) instructing an autonomous vehicle through reinforcement, aiming at on-line improvement, with a human safety driver taking over during the navigation by providing instructions, rewards and penalties to endow an autonomous vehicle to navigate. We expect to consolidate our partnership with a local UK company through this project, so that we will be able to use their autonomous pod to validate our proposed approach.

Person Specification
The successful applicant should have a first class or upper second-class honours degree or equivalent qualification in Computer Science or Electrical/Electronic/Computer Engineering degrees or an M.Sc. degree in one of these mentioned areas.  Preferred skill requirements include knowledge of machine learning, artificial perception and/or robotics.

We would particularly like to encourage applications from women seeking to progress their academic careers. Aston University is committed to the principles of the Athena SWAN Charter, recognised recently by a prestigious Silver Award to EAS, and we pride ourselves on our vibrant, friendly and supportive working environment and family atmosphere.

Contact information
For formal enquiries about this project contact Dr Diego R. Faria by email at d.faria at aston.ac.uk

Submitting an application
Only through the online system (it's not going to be accepted by email). Details of how to submit your application, and the necessary supporting documents can be found at: https://urldefense.proofpoint.com/v2/url?u=https-3A__www2.aston.ac.uk_eas_research_prospective-2Dresearch-2Dstudents_how-2Dto-2Dapply&d=DwIF-g&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=fEkGu4OJpB7QO0gsd5sl1Ot98oBtLntbviaSid22F04&s=Mdah9IovcJLhcs6nwGdY_Zz4w9sAR_YK-ozoy0y1IVU&e= 

The application must be accompanied by a short (2-3 pages) “research proposal” statement. An original proposal is not required as the initial scope of the project has been defined, however candidates should take this opportunity to explain how their knowledge and experience will benefit the project and should demonstrate their familiarity with some relevant research literature.

If you require further information about the application process please contact the Postgraduate Admissions team at seasres at aston.ac.uk

If you have any other questions, please do not hesitate to contact HR via recruitment at aston.ac.uk

Best Regards
Diego


_____________________________________
Dr Diego Resende Faria
Senior Lecturer (Associate Professor) in Computer Science
School of Engineering & Applied Science
Aston University, Birmingham, B4 7ET, UK
Room MB 211-D,  Tel: +44 (0) 1212 044 868
https://urldefense.proofpoint.com/v2/url?u=http-3A__cs.aston.ac.uk_-7Efariad&d=DwIF-g&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=fEkGu4OJpB7QO0gsd5sl1Ot98oBtLntbviaSid22F04&s=WkWjoB4t2AYR4EVhe3RIAh9XFM6RLthixYiQvLoHz84&e= 



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