PhD Candidate in Explainable AI for Synthetic Media Analysis
Gjøvik16 dagar kvar
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Video: https://youtu.be/Xt-yHCN5QS0About the position
We have a vacancy for a PhD candidate in machine learning at the Department of Computer Science (IDI), NTNU, Gjøvik. The position is a three-year full-time doctoral appointment and will be based at Gjøvik campus.
Are you motivated to take a step towards a doctorate and open up exciting career opportunities?
As a PhD candidate with us, you will work towards your doctorate in a strong international research environment and gain experience that supports a future career in higher education, research, or knowledge-intensive industry.
The position reports to the Unit Leader of Colorlab.
About the project
The research will address the growing need for reliable methods that can assess the authenticity of visual media and provide understandable evidence for model decisions. The candidate will investigate how general-purpose pretrained visual and multimodal representations can be adapted for analysing authentic, synthetic, and manipulated images or videos.
The work will combine predictive performance with explainability, uncertainty estimation, robustness, and generalization. Attention will be paid to performance across datasets, content sources, generation methods, and real-world transformations such as compression, resizing, and re-encoding.
The final scientific scope will be refined together with the successful candidate and in dialogue with the relevant academic and external research environments.
Duties of the position
- Develop machine-learning methods for detecting, localizing, or characterizing synthetic and manipulated visual content.
Study representations learned by large pretrained visual or multimodal encoders, including probing, adaptation, fusion, and efficient fine-tuning strategies.
Design explanation methods that connect predictions to meaningful spatial, temporal, frequency-domain, semantic, or example-based evidence.
Evaluate robustness and transfer to unseen datasets, content sources, and manipulation processes, including realistic post-processing and distribution shifts.
Investigate uncertainty, calibration, and reliability measures that can support responsible human decision-making.
Develop reproducible benchmarks and evaluation protocols covering predictive quality, explanation fidelity and stability, and computational efficiency.
Publish results in peer-reviewed international venues and present the research at conferences, consortium meetings, and to interdisciplinary audiences.
Participate in doctoral training, research-group activities, and collaboration with relevant academic and external research environments.
Be prepared for changes to your work duties after employment.
Required selection criteria
- You must meet the requirements for admission to the IE faculty's Doctoral Programme, see Section 6-1 of the PhD regulations for more information.
You must have a master's degree or equivalent in computer science, artificial intelligence, machine learning, computer vision, or signal processing. The degree must include a substantial independent project equivalent to a master's thesis.
You must have a strong and relevant academic background from your previous studies and have an average grade from your master's degree study, or equivalent education, equal to B or better compared with NTNU's grading scale. Applicants with a weaker academic background may be considered if they can document that they are exceptionally suited for PhD education, for example through relevant work experience and/or peer-reviewed academic work.
You must have good knowledge of machine learning and deep learning, with a sound understanding of experimental research methodology documented through coursework, projects, or publications.
You must have strong programming skills in Python and experience with a modern deep-learning framework such as PyTorch or TensorFlow.
You must have good written and oral English communication skills.
The appointment is to be made in accordance with NTNUs guidelines for recruitment positions for general criteria for the position.
Preferred selection criteria
- Experience with computer vision, video analysis, self-supervised learning, vision transformers, or multimodal learning.
Experience with pretrained representation models, transfer learning, parameter-efficient adaptation, or representation probing.
Knowledge of media forensics, synthetic-content analysis, manipulation localization, or related authenticity problems.
Knowledge of explainable AI, uncertainty estimation, calibration, out-of-distribution detection, adversarial robustness, or causal analysis.
Experience with causal inference, counterfactual explanations, or uncertainty quantification in deep learning
Evidence of high quality scientific writing, publications, a strong master's thesis, research software, or relevant open-source contributions.
Prior experience working across geographically distributed teams.
Personal characteristics
To complete a doctoral degree, it is important that you are able to:
Show strong motivation for research and a willingness to engage deeply with challenging scientific questWork independently while contributing constructively to an interdisciplinary team.
Work analytically, systematically, and reliably, with attention to scientific quality, reproducibility, and continuous learning.
Communicate, present, and collaborate effectively, including explaining technical ideas clearly and presenting research results to interdisciplinary audiences and consortium partners.
Emphasis will be placed on personal qualities.
We offer
- Stimulating research tasks at the intersection of machine learning, visual computing, and explainable AI.
A strong international academic environment with access to relevant research infrastructure and expertise.
Opportunities for collaboration, professional development, and participation in leading scientific venues.
An open and inclusive working environment with committed colleagues
Working capital that can be used to implement the project
Career guidance and follow-up during the PhD period
Favorable terms as a member of the Norwegian Public Service Pension Fund (SPK)
Free Norwegian language training at a basic level (A2)
As a PhD Candidate at NTNU, you will have access to employee benefits.
Diversity
Diversity is a strength, and at NTNU we aim to be an employer that reflects the diversity in society and that makes use of the potential of the population's collective skills. Our vision is Knowledge for a better world and our values are creative, critical, constructive and respectful. We believe that an organization that is equal, diverse and gender-balanced is essential for us to achieve our goals.
We strive to attract employees with different skills, life experiences and perspectives to contribute to even better problem solving of our societal mission in research and education.
If you think this position is relevant and interesting, we encourage you to apply, regardless of gender, functional ability and cultural background, or whether you have been out of work for a period of time.
At NTNU we want to increase the proportion of women in scientific positions. We have a number of measures to promote equality.
Salary and conditions
In the position of PhD Candidate, code 1017, your gross salary will normally be NOK 580 000,-per annum depending on qualifications and seniority. A 2% statutory contribution to the State Pension Fund is deducted from the salary.
The employment period is three years, with the full period dedicated to doctoral work.
For employment as a PhD candidate, it is a prerequisite that you gain admission to the relevant NTNU PhD programme within three months of your employment contract start date and participate in the organized doctoral programme throughout the period of employment.
As an employee at NTNU, it is important that you keep yourself up to date with academic and organizational changes and adapt to them.
For the necessary professional and social interaction, it is a prerequisite that you are physically present and available to the institution on a daily basis.
The appointment is carried out in accordance with the principles of the State Employees Act, and Export control (legislation that regulates the export of knowledge, technology and services). Candidates who, after assessment of the application and attachments, are considered to bein conflict with the criteria in the latter act, will not be able to be employed.
About the application
The application and supporting documents must be submitted electronically through Jobbnorge.no. The documents must be in English or a Norwegian/Scandinavian language.
Please note: the application will only be assessed on the basis of the information we have received by the application deadline. Therefore, make sure that your application clearly shows how your skills and experience meet the criteria described above. The application and all attachments must be sent electronically via Jobbnorge.no. If you are invited to an interview, you must bring certified copies of certificates and diplomas upon request.
The application must include:
• All transcripts and diplomas for Bachelor's and Master's degrees, also diplomas supplement if you have this.
• CV.
Copy of Master's thesis.
Research plan containing proposals for an overall description of research questions, theoretical perspectives, methodological design for the project and progress plan (maximum 1500 words/4 pages).
Short letter of motivation (400 words/1 page).
Possibly publications etc. other relevant research work.
Names and contact information of three relevant referees.
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. If your institution uses “diploma supplement” (normal for most European institutions), you must attach this. A description of the documentation required can also be found here. If you already have a statement from Norwegian Directorate for Higher Education and Skills (HK-dir), please attach this as well.
Joint work will be considered. If it is difficult to identify your contribution to joint work, you must attach a brief description of your participation.
When assessing the best qualified, we emphasize necessary qualifications such as education, experience and personal suitability. Motivation for the position, ambitions, and potential for research will also count when assessing the candidates.
NTNU recognizes a wide range of academic contributions and has committed itself to The San Francisco Declaration on Research Assessment and CoARA (responsible assessment of research and recognition of a greater breadth of academic contributions in accordance with NTNU's social mission).
General information
A public list of applicants with name, age, job title and municipality of residence is prepared after the application deadline. If you wish to be exempt from entry on the public applicant list, this must be justified. Assessment will be made in accordance with current legislation. You will be notified if the exemption is not granted.
If you think this position looks interesting and in line with your qualifications, you are welcome to apply.
If you have questions about the position, please contact Professor Kiran Raja, [+47 61135374 and kiran.raja@ntnu.no.
If you have questions about the recruitment process, please contact HR, hr@idi.ntnu.no.
Application deadline: 21.09.2026
Gjøvik, a charming town by the shores of Lake Mjøsa, is known for its beautiful nature and rich cultural offerings. The town boasts a vibrant community with excellent schools, modern healthcare services, and a variety of recreational activities. With its strategic location, just a short drive from Oslo, Gjøvik combines the best of urban life and natural experiences. Here, you'll find a dynamic business environment and exciting career opportunities in a setting that values work-life balance.