PhD Candidate in Explainable AI for Synthetic Media Analysis

Arbeidsplassen; Gjøvik; On-site; NOK 522,000 - 638,000; Full time

Benefits offered by this job

Stimulating research tasks

International academic environment

Collaboration opportunities

Inclusive working environment

Working capital for project

Career guidance

SPK pension

Norwegian language training

Job summary

NTNU in Gjøvik invites applications for a three-year, full-time PhD candidate position in machine learning at the Department of Computer Science (IDI). The successful candidate will pursue a doctoral degree in a strong international research environment and contribute to authenticating visual media using general pretrained representations.

The role focuses on ML method development, explainability, uncertainty, and robust evaluation, with opportunities to publish and present at leading venues.

Qualifications

• Master's degree or equivalent in computer science, artificial intelligence, machine learning, computer vision, or signal processing with substantial independent project.
• Strong background in ML and deep learning with experimental methodology.
• Good written and oral English communication skills.

Responsibilities

• Develop ML methods for detecting, localizing, or characterizing synthetic and manipulated visual content.
• Study representations learned by large pretrained visual or multimodal encoders and adaptation strategies.
• Design explanation methods that connect predictions to spatial, temporal, frequency-domain, or semantic evidence.
• Evaluate robustness and transfer to unseen datasets, content sources, and manipulation processes.
• Investigate uncertainty, calibration, and reliability measures for responsible decision-making.
• Develop reproducible benchmarks and evaluation protocols covering predictive quality and efficiency.
• Publish results in peer-reviewed venues and present at conferences and meetings.
• Participate in doctoral training, research-group activities, and collaboration with external environments.

Skills

Python programming

Machine learning

PyTorch or TensorFlow

English communication

Education

Master's degree in CS/AI/ML/vision

Tools

PyTorch

TensorFlow

Job description

This is NTNU

NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters in Trondheim.

At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world.

You will find more information about working at NTNU and the application process here.

Video: https://youtu.be/Xt-yHCN5QS0

About 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 isKnowledge for a better world andour 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.

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.