About the position

The Norwegian Open AI Lab and Data and Artificial Intelligence group currently has two vacancies in the field of Explainable Artificial Intelligence (XAI). The positions are part of the EXAIGON project.

Each position can be filled by 

• A PhD fellow fully funded for 3 years (or 4 with optional academic duties).
• A postdoc fully funded for 2 years.

About the project

The recent rapid advances of Artificial Intelligence (AI) hold promise for multiple benefits to society in the near future. AI systems are becoming ubiquitous and disruptive to industries such as healthcare, transportation, manufacturing, robotics, retail, banking, and energy. According to a recent European study, AI could contribute up to EUR 13.3 trillion to the global economy by 2030; EUR 5.6 trillion from increased productivity and EUR 7.73 trillion from opportunities related to consumer experience. However, in order to make AI systems deployable in social environments, industry and business-critical applications, several challenges related to their trustworthiness must be addressed first.

Most of the recent AI breakthroughs can be attributed to the subfield of Deep Learning (DL), based on Deep Neural Networks (DNNs), which has been fuelled by the availability of high computing power and large datasets. Deep learning has received tremendous attention due to its state-of-the-art, or even superhuman, performance in tasks where humans were considered far superior to machines, including computer vision, natural language processing, and so on. Since 2013, Deep Mind has combined the power of DL with Reinforcement Learning (RL) to develop algorithms capable of learning how to play Atari games from pixels, beating human champions at the game of Go, surpassing all previous approaches in chess, and learning how to accomplish complex robotic tasks. Similarly, DL technology has been used in combination with Bayesian Networks (BNs), resulting in Deep Bayesian Networks (DBNs), a framework that dramatically increases the usefulness of probabilistic machine learning. Despite their impressive performance, DL models have drawbacks, with some of the most important being lack of transparency and interpretability, lack of robustness, and inability to generalize to situations beyond their past experiences. These are difficult to tackle due to the black-box nature of DNNs, which often end up having millions of parameters, hence making the reasoning behind their predictions incomprehensible even to human experts. In addition, there is a need to better understand societal expectations and what elements are needed to ensure societal acceptance of these technologies.

Explainable AI (XAI) aims at remedying these problems by developing methods for understanding how black-box models make their predictions and what are their limitations. The call for such solutions comes from the research community, the industry and high-level policy makers, who are concerned about the impact of deploying AI systems to the real world in terms of efficiency, safety, and respect for human rights. In order for XAI to be useful in business-critical environments and applications, it should not be limited to algorithm design because the experts who understand decision-making models the best are not in the right position to judge the usefulness and structure of explanations. It is necessary to enhance XAI research by incorporating models of how people understand explanations, and when explanations are sufficient for trusting something or someone. Such models have been researched for many years by philosophers, social and cognitive psychologists, and cognitive scientists. It becomes evident that significant interdisciplinary contributions are needed for AI systems to be considered trustworthy enough for deployment in social environments and business-critical applications.

The EXAIGON (Explainable AI systems for gradual industry adoption) project (2020-2024) will deliver research and competence building on XAI, including algorithm design and human-machine co-behaviour, to meet the society’s and industry’s standards for deployment of trustworthy AI systems in social environments and business-critical applications. Extracting explanations from black-box models will enable model verification, model improvement, learning from the model, and compliance to legislation.

EXAIGON aims at creating an XAI ecosystem around the Norwegian Open AI-Lab, including researchers with diverse background and strong links to the industry. The project is supported by 7 key industry players in Norway who will provide the researchers with use cases, including data, models and expert knowledge. All involved researchers will work closely with each other, the industry partners, and researchers already working on relevant topics at NTNU, hence maximizing the project’s impact and relevance to the real world.

The positions report to Professor John Krogstie (Head of the Computer Science Department).

Duties of the position

Main duties and responsibilities

Position 1 – CBR: XAI methods using Case-Based Reasoning. Case-Based Reasoning (CBR) can take advantage of human experiences in decision making by incorporating those experiences. This capability can be used to build explainable CBR systems (model-specific) as well as explaining other AI system (model-agnostic). Both approaches will be researched in the position with a focus on creating explainable CBR systems. It is expected that the successful candidate will build prototypes for various approaches and evaluates them with the collaborators in the project. For this position, the selection committee will prioritize candidates with previous knowledge in Case-Based Reasoning and a strong knowledge of theoretical and hands-on experience with modern machine learning. It is expected that the successful candidate is a skilled Python programmer, and experience with Java software development is an advantage.

The candidate should have the following qualifications:

• Strong background in one or more of the following areas: Artificial Intelligence or Machine Learning.
• Knowledge (practical or theoretical) of Case-Based Reasoning.
• Excellent programming skills - in one or more of the following languages: Python, Java.
• Research-oriented master thesis and/or PhD thesis within the area of Artificial Intelligence.

The following qualifications will be considered as an advantage:

• To communicate with external project partners, excellent written and oral Norwegian language skills are a plus.
• Ability to work independently as well as collaboratively.
• Knowledge of current Machine Learning frameworks such as scikit-learn, Tensorflow, etc.

Position 2 – ProbAI: XAI methods using Probabilistic AI techniques.

Probabilistic AI investigates the intersection between probabilistic graphical models and deep learning. Such models are often called deep Bayesian networks (DBNs). Injecting probabilistic thinking into DNNs has several benefits, including robustness against overfitting and resilience against adversarial attacks. Furthermore, DBNs can quantify uncertainty in their predictions, also in ways that include model and parameter uncertainty. DBNs will to some extent cater to a causal interpretation, which provides an efficient and robust language for explaining inferences. In EXAIGON we aim to utilize these features in order to generate understandable and trustworthy explanations for model-predictions from probabilistic AI models. Furthermore, we will investigate how techniques for explanations and sensitivity analysis used for traditional Bayesian networks carry over to Probabilistic AI models. We will first consider general strategies for generating explanations from DBNs, and later apply the most promising techniques in industrial settings.

The candidate should have the following qualifications:

• Strong background in one or more of the following areas: Statistics or Machine Learning.
• Theoretical and hands-on experience with modern machine learning.
• Excellent programming skills - in one or more of the following languages: Python, Julia.
• Research-oriented master thesis and/or PhD thesis within the area of Artificial Intelligence or Mathematical statistics

The following qualifications will be considered as an advantage:

• To communicate with external project partners, excellent written and oral Norwegian language skills are a plus.
• Ability to work independently as well as collaboratively.
• Knowledge of current deep learning frameworks such as Pytorch, Tensorflow, etc.

Required selection criteria

The PhD-position's main objective is to qualify for work in research positions. The qualification requirement is completion of a master’s degree or second degree (equivalent to 120 credits) with a strong academic background in Artificial Intelligence and/or Machine Learning with a grade of B or better in terms of NTNU's grading scale. Applicants with no letter grades from previous studies must have an equally good academic foundation. Applicants who are unable to meet these criteria may be considered only if they can document that they are particularly suitable candidates for education leading to a PhD degree.

Postdoctoral research fellowships are qualification positions in which the main objective is qualification for work in academic positions. Completion of a Norwegian doctoral degree in Artificial Intelligence or corresponding foreign doctoral degree recognized as equivalent to a Norwegian doctoral degree is required. The candidate is also required to have a publication record and research experience. 

The appointment is to be made in accordance with the regulations in force concerning State Employees and Civil Servants and 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  

Personal characteristics

• Curious. 
• Team player. 
• Goal focused. 
• Persistent.

We offer

• 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

PhD candidates are remunerated in code 1017, and are normally remunerated at gross from NOK 482 200 per annum before tax, depending on qualifications and seniority. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund.

The period of employment is 3 years (or 4 with optional academic duties).

Appointment to a PhD position requires admission to the PhD programme in Computer Science. As a PhD candidate, you undertake to participate in an organized PhD programme during the employment period. A condition of appointment is that you are in fact qualified for admission to the PhD programme within three months.

Postdoctoral candidates are remunerated in code 1352, and are normally remunerated at gross from NOK 545 300 per annum before tax, depending on qualifications and seniority. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund.

The period of employment for a postdoctoral candidate is 2 years.

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 on a daily basis.

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 that the applicant would like to be considered in the evaluation must accompany the application. Please note that applications are only evaluated based on the information available on the application deadline. You should ensure that your application shows clearly how your skills and experience meet the criteria which are set out above. 

The application must include:

• A cover letter stating for which position the candidate applies (Position 1: CBR or Position 2: ProbAI), and if the application is for a PhD or postdoctoral fellowship.
• Curriculum vitae (CV) with information about the candidate’s prior training, exams, and work experience.
• Certified copies of transcripts and diplomas.
• Applicants from universities outside Norway are kindly requested to send a diploma supplement or a similar describes in detail the study and grading system and the rights for further studies associated with the obtained degree.
• Research statement (max. 3 pages) including
  • A short presentation of the motivation for the position.
  • How the applicant sees his/her background suitable.
  • The applicant's view of research challenges within the area of the position (either Position 1: CBR or Position 2: ProbAI) for the PhD/postdoctoral position, as well as his/her theoretical and methodological approach to the challenges.
• Names and contact information of at least 2 reference persons.
• If you apply for a PhD fellowship, we require a copy of the master thesis (in PDF), or, for those who are near to completion of their MSc, an extended abstract combined with a statement of how and when the applicant plans to complete the thesis (1 page, delivery of master thesis no later than August 31st, 2021).
• If you apply for a postdoctoral position, we require a copy of the PhD thesis (in PDF), or, for those who are near to completion of their PhD, an extended abstract combined with a statement of how and when the applicant plans to complete the thesis (1 page, delivery of PhD thesis no later than August 31st, 2021).

If all, or parts, of your education has been taken abroad, 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.

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.

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. 

NTNU is committed to following evaluation criteria for research quality according to The San Francisco Declaration on Research Assessment - DORA.

General information

Working at NTNU

A good work environment is characterized by diversity. We encourage qualified candidates to apply, regardless of their gender, functional capacity or cultural background. 

The city of Trondheim is a modern European city with a rich cultural scene. Trondheim is the innovation capital of Norway with a population of 200,000. The Norwegian welfare state, including healthcare, schools, kindergartens and overall equality, is probably the best of its kind in the world. Professional subsidized day-care for children is easily available. Furthermore, Trondheim offers great opportunities for education (including international schools) and possibilities to enjoy nature, culture and family life and has low crime rates and clean air quality.

As an employee at NTNU, you must at all times adhere to the changes that the development in the subject entails and the organizational changes that are adopted.

In accordance with The Public Information Act (Offentleglova), your name, age, position and municipality may be made public even if you have requested not to have your name entered on the list of applicants.

If you have any questions about position 1 CBR, please contact Kerstin Bach, telephone +47 735 97410, email kerstin.bach@ntnu.no, or position 2 ProbAI, please contact Helge Langseth, telephone +47 735 96488, email helge.langseth@ntnu.no, or the Head of Department Professor John Krogstie, email john.krogstie@ntnu.no. 

Please submit your application electronically via jobbnorge.no with your CV, diplomas and certificates. Applications submitted elsewhere will not be considered. Diploma Supplement is required to attach for European Master Diplomas outside Norway. Chinese applicants are required to provide confirmation of Master Diploma from China Credentials Verification (CHSI).

If you are invited for interview you must include certified copies of transcripts and reference letters. Please refer to the application number 2020/24842 when applying.

Application deadline: August 15th, 2021

NTNU - knowledge for a better world

The Norwegian University of Science and Technology (NTNU) creates knowledge for a better world and solutions that can change everyday life.

Department of Computer Science

We are the leading academic IT environment in Norway, and offer a wide range of theoretical and applied IT programmes of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software engineering, information systems, learning technology, HCI, CSCW, IT operations and applied data processing. The Department has groups in both Trondheim and Gjøvik. The Department of Computer Science is one of seven departments in the Faculty of Information Technology and Electrical Engineering 

Deadline 15th August 2021
Employer NTNU - Norwegian University of Science and Technology
Municipality Trondheim
Scope Fulltime (2 jobs)
Duration Temporary
Place of service Campus Gløshaugen