Student - exjobb

Background We, at Trustworthy AI team at Mimer, are developing a Trustworthy AI self‑assessment tool that helps organisations translate Trustworthy AI principles and regulatory requirements into practical, and actionable processes. Our work builds on established frameworks such as ALTAI (Assessment List for Trustworthy AI) and aligns closely with the EU AI Act technical requirements, supporting organisations, particularly SMEs, in understanding and navigating requirements when developing and deploying AI systems. As part of this effort, we are looking for an intern to contribute to both the technical development and the assessment design process, working at the intersection of technology, regulation, and structured self‑assessment methodologies. Goals The goal of this internship is to contribute to the development of a practical and scalable Trustworthy AI self‑assessment tool that enables organisations to translate Trustworthy AI principles and EU AI Act requirements into actionable processes. In doing so, the role aims to strengthen the connection between technical implementation (Python/Django) and structured governance frameworks, explore effective methodologies for self‑assessment workflows, and ultimately contribute to building an accessible and valuable tool for organisations. Responsibilities As an intern, you will contribute to the ongoing development, evaluation and improvement of a Trustworthy AI self‑assessment tool, working at the intersection of software development, AI governance, and structured evaluation processes. Your responsibilities will broadly include: * Work closely with a multidisciplinary team (technical, research, and policy perspectives) * Support the iterative improvement of a web-based application built with Python and Django based on feedback and team insight * Contribute to structuring and managing assessment content within the application (e.g., questions, mappings, workflows) and work with structured questionnaire-based assessment frameworks Good to have: * Participate in discussions around self-assessment methodologies and workflows Qualifications You are someone who is: * A masters student or graduate * Comfortable in working with python and Django * Comfortable reading and discussing structured text (policy/technical) * Thrives in team work, engaging discussion and critical analysis and multi-disciplinary setup * Proficient in English, both spoken and written Good to have: * Understand self-assessment, and questionnaire based processes * Swedish language skills What You Will Gain? * The opportunity to work in a multidisciplinary environment alongside researchers, engineers, and policy experts at the intersection of AI, technology, and regulations. * Exposure to real-world Trustworthy AI challenges, including the operationalisation of principles. * Insight into ongoing European standardisation efforts (e.g., CEN‑CENELEC). * First-hand experience engaging with the EU AI Act technical requirements. * Practical experience contributing to a live, evolving software system, combining technical development with structured assessment methodologies. Welcome with your application! To know more, please contact Nishat Mowla (nishat.mowla@ri.se). Applications should include a brief personal letter, CV/resume, and recent transcript of records. Candidates are encouraged to send in their application as soon as possible but at the latest by 30th of August, 2026. Suitable applicants will be interviewed as soon as applications are received. About the position * City: Umeå/Sundsvall/Gothenburg (hybrid position) * Contract type: Temporary position (6 months) * Job type: Internship * Contact person: Nishat Mowla, 073 051 19 37 * Last application date: 2026-08-30

Background We are offering a Master’s thesis or possible internship within digital twins, eXtended reality, robotics, & autonomous systems in the area of computer vision, 3D graphics plus AI-driven scene representation. We are focusing on the emerging field of 3D Gaussian Splatting. It has gained attention as a powerful alternative to Neural Radiance Fields for high-quality, real-time rendering of complex 3D environments. By representing scenes as collections of anisotropic Gaussian primitives, the method enables photorealistic rendering with significantly improved efficiency and interactivity. Your work will be algorithm development, implementation, experimentation, and scene evaluation using GPU-based frameworks. Thesis Focus * Real-time 3D scene reconstruction * Dynamic Gaussian Splatting for moving objects and environments * Compression and optimization of Gaussian representations Work tasks * State of the art survey, including code bases in this area * Practical skills in Python/C++, CUDA, and deep learning frameworks * A demonstration using a moving platform. 3D rendering that a camera mounted on the platform, presenting on a 3D display. Desired background We are looking for very motivated students with interests in: * Computer vision / graphics * Machine Learning * Experience with PyTorch, OpenGL, CUDA, or 3D reconstruction, is very desirable. Your background Studying at a Swedish university, you should have a solid technical background + machine learning. Programming in at least 2 languages. Python+1 from Java, Rust, or C++. Other information You will be given an office at RISE, Kista expected there 2-3 days / week. Paid 1333 SEK per ECTS, tax deductible, paid on satisfactory oral + written thesis defense. Start time Sept 2026. Contact Ian Marsh, Ph.D, Senior Researcher, ian.marsh@ri.se Union representatives: For further information about labor unions, please contact the union representative, Ingemar Petermann, SACO, +46 10 228 41 22 and Linda Ikatti, Unionen, +46 10 516 51 61. Location: Kista, Tel: +46 70 772 1536

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

Do you care for our future? Do you think that sustainability must be a foundation of today’s digital society? Do you have an interest in printed electronics and sustainability? Do you enjoy working in a dynamic team? We are looking for a Master of Science student for a Master Thesis project in RISE. About GreenWave 2 GreenWave 2 is a collaborative innovation project aimed at developing a sustainable European value chain for graphite production based on forest-derived biomass. The project focuses on converting lignin, a by-product from the pulp and paper industry, into battery-grade graphite through an energy-efficient microwave graphitization process. By coating lignin onto silicon carbide (SiC) substrates and applying rapid microwave heating, the project seeks to significantly reduce the energy consumption and carbon footprint associated with conventional graphite production. As graphite is a critical raw material for lithium-ion batteries and is currently sourced largely from fossil-based or non-European supply chains, GreenWave 2 contributes to increased resource security, industrial resilience, and climate neutrality. The project also evaluates the performance of the produced graphite in battery applications while assessing techno-economic feasibility and environmental sustainability, supporting the development of a competitive and sustainable graphite industry within Europe. Background Welcome to RISE Emerging Technologies in Norrköping. In close collaboration with your home university, we can offer you diploma work in our exciting R&D environment. During the project work you will be a part of our team at RISE and will be supported by supervisors both from RISE and your home university throughout the project. Most of the practical laboratory work will be carried out in our facility Printed Electronics Arena in Norrköping. Project research topic Life Cycle Analysis (LCA) is becoming an integral component of product development in the electronics industry. Consumers want to know the environmental impacts of the electronic products they purchase, ranging from their carbon footprint and energy efficiency to end-of-life considerations and recyclability. Sustainability of electronic products is also high on the agenda of policy makers who want to guarantee a safe, sustainable and secure future for their constituents. This thesis project will focus on developing a Life Cycle Assessment (LCA) model for a novel microwave-based graphitization process that converts lignin, a renewable by-product from the forest industry, into battery-grade graphite. The work will include mapping material and energy flows, collecting process data from laboratory and pilot-scale experiments, and evaluating the environmental impacts of the technology. The developed model will be used to identify key environmental hotspots and compare the sustainability performance of bio-based graphite production with conventional fossil-based and mined graphite supply chains. Project activities This diploma work comprises of: * Understanding the basics of the role of graphite and hard carbon in batteries * Understanding synthetic graphite production (both generic and project specific) * Defining the scope of the LCA study * Making a list of the environmental inputs and outputs of processes with the help of a researcher from RISE and creating a comprehensive LCA model in the LCA software SimaPro * Performing LCA calculations * Interpretation of the LCA results * Presenting the environmental finding to internal and possibly external audiences and potential for optimisation Student profile You have an interest in environmental sustainability, material production processes, electronics and/or science. You should be MSc student in the fields of environment, physics, materials or chemistry science, or related subject. You need to be enthusiastic, curious, and keen to develop your skills, enjoy working in a dynamic international team and with good communication skills. Excellent language skills in spoken and written (scientific and technical) Swedish and English is a strong plus. Location and start date RISE Emerging Technologies located in Norrköping, Södragrytsgatan 4. Master thesis start in September 2026. Supervisory team at RISE Main supervisor: Jesper Edberg Co-supervisor: Tatjana Karpenja Interested? Send in your application (CV and motivation letter) no later than August 14th.

Description Vehicle-to-Grid (V2G) is a technology that allows electric vehicles (EVs) to not only draw power from the grid for charging but also feed stored energy back to the grid, supporting energy balance, grid stability, and renewable integration. V2G promises valuable grid services, but the gap between theoretical control concepts and actual hardware performance is not fully explored. The main objective of this thesis is to experimentally evaluate a V2G system under realistic operating conditions. An advanced real-time simulation hardware platform will be employed in this thesis project, serving as both the control system and the digital model of the grid and vehicle-side systems. Such a platform represents an intermediate step between offline simulation and real vehicle operation. Execution Step 1 – Experimental setup * Set up and configure the real-time simulation hardware and software platform. * Operate the system to demonstrate stable bidirectional charging in the lab. * Perform experimental validation to confirm successful commissioning and reliable system operation. Step 2 – Experimental operation * Define and implement selected existing V2G bidirectional charging strategies. * Evaluate the performance of these strategies by analyzing how the system tracks applied charge/discharge power commands (steps, ramps, and realistic profiles). * Quantify system efficiency and losses, including round-trip efficiency, standby losses, and operational stability limits. Expected outcome The thesis will deliver a validated experimental setup along with quantitative evaluation of system capabilities and limitations. Required skills * Basic control systems and power electronics * MATLAB/Simulink modeling * Hands-on lab work and data analysis * Interest in real-time systems and experimentation. Experience with dSpace is very meriting. Personal interest in real-time control and energy system simulation is considered a merit. The work will be carried out in Gothenburg. The thesis is recommended for one or two students. The application deadline is September 30th, 2026. For questions, please contact: Jonas Hellgren – RISE jonas.hellgren@ri.se Xiaoliang Huang – RISE xiaoliang.huang@ri.se