
RISE Research Institutes of Sweden AB · Stockholm
Background Autonomous vehicles rely on AI models trained on large-scale, multi-modal sensor data. As vehicle platforms evolve, changes in sensors and hardware ...
Background
Autonomous vehicles rely on AI models trained on large-scale, multi-modal sensor data. As vehicle platforms evolve, changes in
sensors and hardware often require updating or retraining these models, which is costly and time-consuming. A key challenge is
therefore how to efficiently transfer knowledge between models operating under different configurations.
This thesis is part of the research project DREAM – Distributed, Robust and Efficient AI for Autonomous Vehicles. The topic is
highly relevant for enabling scalable and efficient AI development in next-generation autonomous driving systems.
Description
Sensor data and AI enable cars to detect objects, understand their environment and make decisions about how to respond. When
vehicles are updated and new models are developed, sensors and hardware often change, which in turn also affects the AI models
used. One approach would be to create a new AI model from scratch and collect new data each time the vehicle platform is updated.
A more efficient solution would be to transfer knowledge between models with varying architectures. In this Master’s thesis
project, we aim to investigate knowledge transfer between diverse models and hardware setups, ensuring that learning can continue
even when architectures change. The work will use the Zenseact Open Dataset and also explore knowledge transfer in a federated
learning context.
Main Tasks
In this master thesis project, you will focus on investigating knowledge transfer between diverse models and hardware setups in
collecting extensive new datasets.
signals, geographical positions, and lidar, radar, and camera measurements.
and adaptability in real-time, large-scale deployments.
Qualifications
We are looking for one or two highly motivated students with a good general background in machine learning and computer vision.
Conditions
Welcome with your application!
Send in your application (CV, motivation letter, transcript of records) no later than August 31st.
Background and project description Autonomous vehicles generate massive amounts of multi-modal sensor data, including camera images, lidar point clouds, radar measurements, GPS information, and vehicle control signals. These heterogeneous data sources provide complementary information that is essential for robust perception, localization, and decision-making. However, transferring such large volumes of data to centralized servers is often impractical due to bandwidth limitations, storage costs, privacy concerns, and regulatory constraints. Federated Learning (FL) offers a distributed and privacy-preserving framework that enables multiple vehicles, fleets, or organizations to collaboratively train machine learning models without sharing their raw data. While FL has shown significant promise for autonomous driving applications, its effectiveness is often limited by the availability of high-quality labeled data. Deep learning-based perception modules require high-quality annotations, which are costly and complex to obtain. Self-supervised learning (SSL) offers a solution by leveraging mostly unlabeled data with minimal labels. Early studies show that federated self-supervised training can achieve performance comparable to centralized approaches, with potential improvements as larger unlabeled datasets are used. This thesis project aims to advance federated learning for autonomous vehicles by integrating self-supervised methods with robust aggregation techniques to develop models that are efficient, generalizable, and capable of handling both common and rare driving scenarios, while reducing reliance on manual annotation and avoiding the costs of central data storage. The thesis is part of the research project DREAM – Distributed, Robust and Efficient AI for Autonomous Vehicles. The topic is highly relevant for enabling scalable and efficient AI development in next-generation autonomous driving systems. Main Tasks * In this master thesis project, you will focus on: * A novel self-supervised learning approach to exploit all available data on the central server, even with limited labels * A hybrid federated learning scheme combining self-supervised and supervised techniques, adapted to local and global learning rounds * Validation through extensive comparisons with fully supervised learning within the same federated scheme. * Demonstration of the efficacy of combining self-supervised and supervised learning on the Zenseact Open Dataset (ZoD) under various federated scenarios. * Present findings to the project partners Qualifications We are looking for one or two highly motivated students with a good general background in machine learning and computer vision. The following skills would be essential: * Deep learning * Computer vision * Python programming * Reading scientific papers * Handling complex systems * Federated learning (would be a bonus) Conditions. * Location: RISE, Kista, Stockholm * Applications are reviewed on a rolling basis, apply as soon as possible, but no later than August 31st, 2026. * Starting date: As soon as possible, not later than September 1st, 2026. * Credits: 30 points * Compensation: 39990 SEK upon a successful completion of a high-quality thesis. Supervisors: * Sima Sinaei (RISE) * Henrik Abrahamsson (RISE) Welcome with your application! Send in your application (CV, motivation letter, transcript of records) no later than August 31st. For any questions, please contact: * Sima Sinaei, sima.sinaei@ri.se * Henrik Abrahamsson, henrik.abrahamsson@ri.se
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-yHCN5QS0About the position Are you motivated to take a step towards a doctorate which opens up exciting career opportunities? As a PhD Candidate at the Department of Sociology and Political Science you will work to achieve your doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, both within and outside academia. This PhD position in Political Science offers an excellent opportunity for scientific development through designing, conducting, and disseminating research on a contemporary topic. The goal of the PhD. position is to complete doctoral-level education and to complete a doctoral degree. The position is 4 years, including one year of promotion work such as teaching. The candidate will be part of the active research group Public Policy and Administration at the Department of Sociology and Political Science and be part of a broader international network of researchers working in the field of Public Administration & Management and Digital Government. For more information about the research group, see Public Policy and Administration - NTNU. Your immediate leader will be the Head of Department. About the project Cybersecurity Governance in Critical Public Infrastructures The PhD project will investigate how sector specific institutional contexts shape the governance of cybersecurity in critical public infrastructures. Candidates may focus on selected sectors—such as energy, transport, education, or health—to analyze why governance models differ and how variations in administrative capacity, regulatory design, risk exposure, and public–private collaboration contribute to these differences. The project aims to identify institutional arrangements that enable effective coordination between governmental authorities and private actors and to develop a comparative framework for understanding alternative pathways to effective cybersecurity governance. Applicants are expected to develop a proposal that contributes to this comparative agenda, drawing on relevant theoretical perspectives from public administration, governance, or digital government studies. Qualitative or mixed methods approaches are welcome. Duties of the position • Complete the doctoral education until obtaining a doctorate. • Carry out research of high academic quality within the framework described above, and develop your own approach to the project. • Academic publications and popular science dissemination • Be an active participant in the research group Public Policy and Administration. • Participate in international activities such as conferences, workshops, PhD courses and/or research stays at foreign educational institutions. • Teaching in relevant study programs at the Department. • Be prepared for changes to your work duties after employment. The PhD is obliged to conform with the regulations concerning changes and developments within the position, and/or the organizational changes concerning activities at NTNU. Required selection criteria To be qualified for this position you must fulfill the following criteria. • You must have a master's degree in Political Science, Social Science interdisciplinary digitalisation‑oriented programmes such as Organisation, Digitalisation, Administration and Work (MODAA), Public Management, Public Governance, Public Administration, or equivalent. Your education must correspond to a five-year Norwegian course, where normally 120 ECTS credits have been obtained at master's level included a master’s thesis or equivalent work with a scope of at least 30 ECTS credits. Master's students can apply, but the master's degree must be obtained and documented before starting the position. • You must have a strong academic background from your previous studies and have an average grade from your Master's degree study, or equivalent education, which is equal to B or better compared to NTNU's grading scale. If you do not have letter grades from previous studies, you must have an equally good academic foundation. If you have a weaker grade background, you maybe considered if you can document that you are particularly suitable for a PhD education. • You must meet the requirements for admission to the faculty's Doctoral Programme in Social Science. • Good quality of the project proposal. • Experience with qualitative research as the main methodological approach, with the ability to engage in quantitative statistical analysis when relevant. • Good written and oral skills in English. The appointment is to be made in accordance with NTNUs guidelines for recruitment positions for general criteria for the position. Preferred selection criteria • Demonstrated experience with qualitative data collection, including interviews. • Familiarity with qualitative coding and analysis using tools such as NVivo, Atlas.ti, or similar. • Ability to design and conduct rigorous qualitative research, including sampling strategies, interview protocols, and ethical considerations. • Beneficial additional competence includes knowledge of digital transformation/digital government, especially an interest in or experience with cybersecurity. • The position is ascribed one year of promotion work such as teaching, and good written and oral language skills in Norwegian or another Scandinavian language is an advantage. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: • show curiosity and a strong motivation for the subject • work independently and structured • set goals and make plans to reach them, • creatively approach your subject matter, and present and discuss your research and constructively engage with feedback from colleagues. Emphasis will be placed on personal qualities. We offer Evaluate and remove/add what is relevant for the position. • An exciting job with an important mission in society • Developing tasks in a strong and international professional environment • Career guidance and follow-up during the PhD period • Open and inclusive working environment with committed colleagues • Working expenses funding that can be used to implement the project • Mentor programme as a new employee at NTNU • As a public employee, you have favorable benefits 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. Salary and conditions In the position of PhD Candidate, code 1017, your gross salary will normally be NOK 550 800,-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 4 years including 1 year of career promotion work such as teaching. For employment as a PhD Candidate, it is a prerequisite that you gain admission to the PhD programme in Social Sciences – specialization Political Science within three months of your employment contract start date, and that you participate in an organized doctoral programme through out 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 be in conflict with the criteria in the latter act, will not be able to be employed. About the application The attachments (including a description of your scientific work) must accompany the application as these documents form the basis of the application assessment. The documents must be in Norwegian/a Scandinavian language or English. 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: • Transcripts and diplomas for Bachelor's and Master's degrees • CV • Copy of Master's thesis. If you have recently submitted your Master's thesis, you can attach a draft of the thesis. Documentation of a completed Master's degree must be presented before taking up the position. • Project outline containing proposals for an overall description of research questions, theoretical perspectives, methodological design for the project and progress plan (maximum 1500 words/4 pages) • Possibly certificates, publications etc. other relevant research work • Names and contact information of three relevant referees In addition, applicants are required to answer the position-related screening questions in Jobbnorge when submitting their application. 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 any questions about the position, please contact Associate Professor Barbara Zyzak, telephone: +47 96744330, e-mail: barbara.k.zyzak@ntnu.no. If you have any questions about the recruitment process, please contact HR Advisor Renate Lillian Johansen, e-mail: renate.johansen@ntnu.no. Application deadline: 01.09.2026 For practical information about working at NTNU, please visit this webpage. The city of Trondheim is a modern European city with a rich cultural scene. Trondheim is the tech 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.
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-yHCN5QS0About the position 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 to achieve your doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, in and outside academia. We invite applications for a PhD position funded by NTNU and associated with the Norwegian Maritime AI Center (MAI). The position contributes to Use Case 12 (UC12): Arctic Maritime Operations and addresses a key challenge for maritime AI: supporting safe, reliable, and timely operational decisions in ice‑affected waters under high uncertainty. The PhD will focus on the Marginal Ice Zone (MIZ), where sea ice responds rapidly to wind, waves, and currents, and where existing ice charts and satellite products are often insufficient for short‑term operational planning. The project will develop AI‑enabled and physics‑informed forecasting and decision‑support approaches by combining heterogeneous observations with physics‑based simulation tools, including operationally adapted configurations of the SAMS (Simulation of Arctic Marine Systems) framework. Your immediate leader will be a professor. About the project The Marginal Ice Zone represents one of the most complex and operationally challenging environments in Arctic maritime operations. In the MIZ, wave–ice interaction, ice breakup, and subsequent compaction can rapidly alter navigability. Storm events may lead to fast shifts in the ice edge and sudden extension of the MIZ, requiring timely decisions under significant uncertainty. While satellite observations and ice charts are essential sources of information, their temporal resolution and predictive capability are often insufficient in the MIZ. This motivates the development of short‑term forecasting and scenario‑based tools that explicitly account for fast ice dynamics and uncertainty relevant for operational planning. The PhD addresses Arctic ice navigation as a system‑level challenge, integrating heterogeneous information from onboard sensors such as marine radar and cameras, satellite Earth‑observation products, ice charts, and metocean forecasts. The objective is to produce coherent, continuously updated representations of ice conditions that support short‑term forecasting, nowcasting, and scenario exploration for route planning and operational decision‑making. Physics‑based simulation plays a central role in this research project by enabling propagation of ice conditions in time and exploration of physically plausible scenarios when observations are sparse or delayed. In this project, the SAMS framework will be used in an operationally oriented configuration, focusing on computationally efficient simulation of MIZ processes such as wave‑induced ice breakup and ice‑edge evolution. These simulations will both directly inform forecasting and be used to support AI model training, validation, and interpretability, providing a physically grounded backbone for hybrid AI–physics decision‑support concepts. AI methods will be applied to fuse heterogeneous data sources, learn fast surrogate representations of physics‑based simulations, and quantify uncertainty relevant for operational decisions. The project leverages MAI foundations for AI‑ready data, hybrid modelling, and trusted AI, while tailoring these capabilities to Arctic MIZ conditions. The research will be guided by operationally relevant questions, such as how storms and wave forcing affect short‑term MIZ evolution; under what conditions wave–ice interaction leads to rapid ice breakup or MIZ extension; and how hybrid AI–physics approaches can support dynamic route planning with quantified uncertainty. Emphasis will be placed on time horizons from minutes to days, which are most relevant for maritime operations. Expected outcomes include hybrid AI–physics workflows for MIZ forecasting, AI‑ready datasets derived from observations and simulations, prototype forecasting and scenario‑evaluation components, and contributions to decision‑support concepts compatible with S‑100‑based maritime information products. The work will result in scientific publications and demonstrators aligned with MAI objectives. The PhD will be conducted in close collaboration between NTNU and the Norwegian Meteorological Institute (MET), with active involvement of interested MAI’s user partners such as Equinor and the Norwegian Coastal Administration (NCA). This ensures close alignment between research outcomes, operational needs, and regulatory frameworks. We seek a motivated candidate with a background in engineering, ocean technology, computer science, data science, geophysics, or related discipline, and with a strong interest in AI, modelling, and Arctic maritime operations. Experience with numerical modelling, geospatial data, or machine learning is an advantage. An interest in system‑level thinking and integration of models, data, and AI methods is particularly valued. The appointment will be carried out in accordance with the principles of the State Employees Act and applicable export control regulations governing the transfer of knowledge, technology, and services. Candidates whose background is assessed to be in conflict with these regulations cannot be employed. Duties of the position • Complete the doctoral education until obtaining a doctorate • Carry out research of good quality within the framework described above, including development of models, datasets, and prototype solutions • Academic publications and popular science dissemination • Contribute to research group activities in the group Marine Civil Engineering • Teaching and/or other career-enhancing work (typically corresponding to 25% of the position), to be agreed in more detail with the department • Participate in international activities such as conferences and/or research stays at foreign educational institutions Be prepared for changes to your work duties after employment. Required selection criteria • You must have a relevant Master's degree in engineering, ocean technology, computer science, data science, geophysics or equivalent. Your education must correspond to a five-year Norwegian course, where 120 credits have been obtained at master's level. Master students can apply, but the master's degree must be obtained and documented before starting the position. • You must have a strong academic background from your previous studies and have an average grade from your Master's degree study, or equivalent education, which is equal to B or better compared to NTNU's grading scale. If you do not have letter grades from previous studies, you must have an equally good academic foundation. If you have a weaker grade background, you maybe considered if you can document that you are particularly suitable for a PhD education. • You must meet the requirements for admission to the faculty's Doctoral Programme • Good oral and written presentation skills in English PLEASE NOTE: For detailed information about what the application must contain, see paragraph “About the application”. The appointment is to be made in accordance with NTNUs guidelines for recruitment positions for general criteria for the position. Preferred selection criteria • Strong interest in AI, modelling, and Arctic maritime operations. • Experience with numerical modelling, geospatial data, or machine learning. • An interest in system‑level thinking and integration of models, data, and AI methods. • Experience with high-performance computing • Interest in interdisciplinary research • Good oral and written presentation skills in Norwegian Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: • Strong motivation for research • Ability to work independently and collaboratively • Show curiosity and strong motivation for the subject • Present and discuss your research with other professionals • Get involved and contribute constructively with feedback • Demonstrate strong communication skills Emphasis will be placed on personal qualities. We offer Evaluate and remove/add what is relevant for the position. • An exciting job with an important mission in society • Developing tasks in a strong and international professional environment • Career guidance and follow-up during the PhD period • Open and inclusive working environment with committed colleagues • Working capital that can be used to implement the project • Mentor programme as a new employee at NTNU • 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 550 800,-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 3 years for the doctoral work in addition to 1 year of career promotion work. (A minimum of three work years of the total term period must be dedicated to doctoral work.) For employment as a PhD Candidate, it is a prerequisite that you gain admission to the PhD programme in Civil and Environmental Engineering within three months of your employment contract start date, and that you participate in an organized doctoral programme through out 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 attachments (including a description of your scientific work) must accompany the application as these documents form the basis of the application assessment. The documents must be in Norwegian/a Scandinavian language or English. 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: • Transcripts and diplomas for Bachelor's and Master's degrees • CV • Copy of Master's thesis. If you have recently submitted your Master's thesis, you can attach a draft of the thesis. Documentation of a completed Master's degree must be presented before taking up the position. • Project outline 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 • Possibly certificates • 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 any questions about the position, please contact Professor Raed Lubbad, telephone +47 73 59 45 83, e-mail: raed.lubbad@ntnu.no. If you have any questions about the recruitment process, please contact HR Consultant Oda Aune, e-mail: oda.aune@ntnu.no. Application deadline: 23.28.2026 ----------------- For practical information about working at NTNU, please visit this webpage. The city of Trondheim is a modern European city with a rich cultural scene. Trondheim is the tech 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.