Big Data and Artificial Intelligence For a Sustainable and Carbon-neutral Mobility Future
Position Details (PhD)
The Department of Engineering Cybernetics (ITK) is looking for a Ph.D. candidate in the area of Big Data and Artificial Intelligence for a sustainable and carbon-neutral mobility future. The aim of the Ph.D. will be to develop and exploit big data and artificial intelligence-driven digital twin of urban mobility infrastructures to solve challenges in achieving a carbon-neutral mobility future. A digital twin is defined as a virtual representation of a physical asset or process enabled through data and simulators for real-time prediction, optimization, monitoring, control, and improved decision making. The Ph.D. will work towards developing enabling technologies to instill physical realism in such a digital twin. The enabling technologies will consist of data acquisition, pre-processing, fusion, and postprocessing techniques using an array of physics-based, data-driven, and hybrid models. In addition, the work will also involve the development of tools for communicating insights in a way that facilitates informed public opinion building and decision-making.
The PhD position is part of a new large interdisciplinary initiative called Mobility Lab Elgeseter. The center is divided into the three focus areas 1) stakeholder needs for good mobility, 2) mobility as a system / transportation models, and 3) digital technologies for green mobility, that will work closely together to realize innovative and sustainable future mobility solutions in the urban environment. Within area 3) various enabling technologies will be used to automate the process of building and using digital mobility infrastructure twins (holistic/unified, life cycle, hierarchical, integrated, dynamic/updated, predictive and automated) for collaboration, simulation, value creation, decision support, road object condition monitoring, carbon/energy footprint and automated traffic management etc. (general knowledge will be developed that can be scaled up and used elsewhere). To realize this several PhD candidates with partly overlapping competences will work closely together, each focusing on one of the following areas: 1) baseline Digital Mobility Twin (DMT) using site surveys and available geo-located data, 2) dynamically updated DMT using IoT, sensors, 5G and edge computing, 3) BigData and AI (this PhD position) to create value from all the sensor data sent from the physical twin, e.g. in the form of decision support and automation, 4) autonomy and simulation to train AI agents and simulate “what-if” scenarios, and 5) XR and Visualization to interact with the DMT throughout its life-cycle (construction and use) and increase citizens engagement and feedback before things have been built physically.
Throughout the overall Mobility Lab Elgeseter project there will be tight interaction with the other two focus areas, e.g. provide sensor data and viz. to the two areas and get feedback from area 1) regarding user needs and integrate transportation models from 2) in the DMT.
This PhD project is also part of the PERSEUS doctoral program that will contribute to a smarter, safer, and more sustainable future by approaching important challenges within key enabling technologies (Big data and AI, Digital Twins, Internet of Things, Extended Reality, and Information and Cyber Security). PERSEUS is a collaboration between NTNU, 11 top-level academic partners in 8 European countries, and 8 industrial partners within sectors of high societal relevance. The PERSEUS PhD candidates will have the opportunity to collaborate with researchers in the partner institutions and in other project consortia, and benefit from these collaborative research and education activities. This includes a 2–3 month international stay and a 1-2 month national stay with one of the PERSEUS partners.
The position’s working place will be the NTNU campus in Trondheim and you will report to Prof. Adil Rasheed.
Duties of the position
- submit an application for admission to the PhD-program at NTNU no later than 3 months after the employment
- undertake the necessary courses (30 ECTS) as part of the PhD program
- conduct high quality research and report progress on a regular basis and in agreement with the supervisors
- engage and communicate with both academic partners and public stakeholders within the Mobility Lab Elgeseter project
- actively participate in the design, implementation, and evaluation of the overall digital mobility twin ecosystem
- contribute to the dissemination of the research outcomes through public media, conferences, and publications
- participate in the supervision of master’s students associated with the project
- contribute to the academic environment at department, faculty and NTNU
Required selection criteria
- International mobility requirements: Applicants of any age and any nationality will be eligible as far as they fulfil the mobility requirement of the COFUND program, namely, the applicants must not have resided or carried out their main activity (work, studies, etc.) in the country of the host organization (Norway) for more than 12 months in the 3 years immediately before the call deadline of PERSEUS. Candidates must at the date of the call deadline be within the first four years of their research careers and not yet be awarded a doctoral degree. Full-time equivalent research experience is measured from the date when the candidate obtained the degree entitling him/her to embark on a doctorate (e.g. from the Master’s degree).
- Education requirements: 5 years of higher education (BSC and MSC) or education equal to (300 ECTS). The grade requirements are B or higher (based on NTNU’s grading scale). Master degree must be completed by the call deadline. Education will have to be documented by diploma supplement or equivalent documentation in English and include a description of the educational system. International Relations do require Chinese diplomas to be verified using CHSI. The NTNU Office of International Relations will evaluate degrees and diplomas.
- Language skill requirements: The applicants will be asked to provide evidence of good English language skills, written and spoken. The following certificates may be used as such evidence: TOEFL, IELTS or Cambridge Certificate in Advanced English (CAE) or Cambridge Certificate of Proficiency in English (CPE).
- Export of Knowledge Control: The PhD candidates who, based on a comprehensive first-hand assessment, might come into conflict with legislation governing exports of knowledge, technology and services will not advance further from the first stage in the recruitment process. For further information, please refer to the Export Control Act.
In addition, the candidate must have:
- a master’s degree in cybernetics, mathematics, physics, computer science or equivalent with strong analytical skills
- relevant background and experience within machine learning and / or modern computer vision
- excellent programming skills and good knowledge of key programming languages and frameworks used in machine learning and date science
- experience or interest in game development environment like Unity or Unreal Engine will be appreciated
The appointment is to be made in accordance with Regulations concerning the degrees of Philosophiae Doctor (PhD) and Philosodophiae Doctor (PhD) in artistic research national guidelines for appointment as PhD, post doctor and research assistant
Preferred selection criteria
- have an interest in and relevant experience related to mobility, digitalization, modeling and simulation, BigData, Artificial Intelligence, Virtual/Augmented reality
- have a good understanding of the connection between technology, processes, and human interaction
- excellent written and oral English
- be motivated and ambitious
- ability to work independently as well as in cross disciplinary teams
- ability to interact and collaborate with actors in academia, public sector and industry (e.g. behave respectfully and value the inputs and opinions of others)
- ability to thrive and contribute to the work environment
- be self-motivated, focused and goal-oriented, be committed and keep deadlines, be flexible, reliable, and solution-orientated, be innovation driven, enthusiastic and ambitious
- be scientifically curious and open to new research challenges, demonstrate persistence in addressing technical problems
- good communication and dissemination skills.
- exciting and stimulating tasks in a strong international academic environment
- an open and inclusive work environment with dedicated colleagues
- favourable terms in the Norwegian Public Service Pension Fund
- employee benefits
Salary and conditions
As a PhD candidate (code 1017) you are normally paid from gross NOK 501 200 per annum before tax, depending on qualifications and seniority. However it may be negotiable (increased) depending on high level of qualifications and research experience of the candidate. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund.
The period of employment is 3 years.
Appointment to a PhD position requires that you are admitted to the PhD programme in Engineering Cybernetics within three months of employment, and that you participate in an organized PhD programme during the employment period.
The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criteria in the latter law will be prohibited from recruitment to NTNU. After the appointment you must assume that there may be changes in the area of work.
It is a prerequisite you can be present at and accessible to the institution daily.
About the application
The application and supporting documentation to be used as the basis for the assessment must be in English.
Publications and other scientific work must follow the application. Please note that your application will be considered based solely on information submitted by the application deadline. You must therefore ensure that your application clearly demonstrates how your skills and experience fulfil the criteria specified above.
Please submit your application electronically via Jobbnorge website. Applications submitted elsewhere/incomplete applications will not be considered. Applicants must upload the following documents within the closing date:
- A short cover letter identifying your motivation and suitability for the position (based on the stated selection criteria)
- CV including information relevant for the qualifications, submitted together with an identification document and name, current affiliation, and email address of at least two persons that will serve as a reference for you.
- Certified copies of academic diplomas and transcripts, and 2 reference letters.
- If all, or parts, of your education has been taken outside of Norway, we also ask you to attach documentation of the scope and quality of your entire education, both bachelor`s and master`s education, in addition to other higher education. Description of the documentation required can be found here. If you already have a statement from NOKUT, please attach this as well.
- A research interest description (maximum 2 pages) that includes a short presentation of the motivation for a PhD study, suitability for the position and the applicant’s view towards listed research challenges as well as the theoretical and methodological approach to the challenges.
- Scientific publications (if any). Joint works will be considered. If it is difficult to identify your contribution to joint works, you must attach a brief description of your participation.
We will take joint work into account. If it is difficult to identify your efforts in the joint work, you must enclose a short description of your participation.
In the evaluation of which candidate is best qualified, emphasis will be placed on education, experience and personal and interpersonal qualities. Motivation, ambitions, and potential will also count in the assessment of the candidates.