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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 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 autonomous vehicles. Specifically, you will: * Explore how knowledge can be efficiently transferred between AI models with different architectures. * Evaluate techniques for updating AI models when vehicle sensors or hardware change, without the need for full retraining or collecting extensive new datasets. * Analyze the effectiveness of the proposed approach through experiments using multi-modal sensor data, including vehicle control signals, geographical positions, and lidar, radar, and camera measurements. * Develop and experiment with a federated learning framework that incorporates knowledge transfer to maintain model performance 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. The following skills would be essential: * Deep learning * Federated learning (would be a bonus) * Python programming * Reading scientific papers * Handling complex systems 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: * Henrik Abrahamsson (RISE) * Sima Sinaei (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: * Henrik Abrahamsson, henrik.abrahamsson@ri.se * Sima Sinaei, sima.sinaei@ri.se
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
Jönköping Energi har en samhällsviktig funktion, att förse våra kunder med el, fjärrvärme och stadsnät i vardagen och att utveckla nya smarta lösningar som tar oss in i framtiden. Vi på Energitjänster vill leverera kostnadseffektiva tjänster till fastighetsägare inom områdena värme och kyla, fordonsladdning, sol, lagring och styrning. Vill du vara med och påverka Jönköpings framtida energitjänster genom att skapa kontakt under din studietid? Under hösten kommer vi kunna ta emot en tekniker och den platsen är tillsatt. Under våren 2027 kommer vi kunna ta emot en student som läser till energiingenjör eller liknande. Dessa ansökningar kommer vi hantera under hösten. Om Energitjänster De två teamen inom Energitjänster levererar kostnadseffektiva tjänster till fastighetsägare inom områdena värme och kyla, fordonsladdning, sol, lagring och styrning. Energitjänster har också ansvar för att etablera leveransförmåga för våra nya erbjudanden. De förvaltar och vidareutveckla vår produktkatalog som är helt avgörande för hur vi ska lyckas med energiomställning. Medarbetarna har många olika kompetenser och har roller som servicetekniker, serviceelektriker, projektledare, affärsingenjörer och administratörer. Om dig Vi kan ge dig som tycker att energibranschen är spännande och som studerar en utbildning till t.ex. energiingenjör, fastighetsingenjör eller fastighetstekniker olika möjligheter att komma i kontakt med oss under din studietid. Nyfiken och engagerad är två viktiga egenskaper för oss! Beskriv gärna hur dessa egenskaper visar sig hos dig! Kika gärna på filmen med Mårten! Mer information Välkommen att kontakta Anna via studentsamarbete@jonkopingenergi.se för mer information. På grund av att vi är en samhällskritisk verksamhet kommer du att säkerhetsprövas. Processen för säkerhetsprövningen kan ta upp till 5 veckor, det är därför viktigt att du ansöker i god tid innan din Exjobbs-/LIA-period börjar.
Vill du göra praktik i en samhällsbärande organisation som drivs av hållbarhet och utveckling? Jönköping Energi är inne i en expansiv fas med utveckling och många spännande utmaningar framför sig. Vi kan erbjuda dig en utvecklande LIA-period med extra benefits! Vi erbjuder löpande LIA-platser till studenter som läser drifttekniker inom kraft, värme och process. Under din LIA-period kommer du att få se flera olika delar av vår verksamhet inom värme och kyla och du kommer att kompetensutvecklas genom mötet med våra grymma kollegor. Då Jönköping Energi även har verksamhet inom ladd, sol, fjärrvärme och fiber kommer du även få en viss kunskap inom energisektorn som helhet. Du kommer få en mindre ersättning för din LIA-period och du som visar nyfikenhet, driv och engagemang kommer ha goda möjligheter att erbjudas både sommarjobb och vidare anställning. VI SÖKER DIG SOM läser drifttekniker/processtekniker troligen genom en eftergymnasial yrkesutbildning. Nyfiken och engagerad är två viktiga egenskaper för oss! Beskriv gärna hur dessa egenskaper visar sig hos dig! Titta gärna på filmen med Milton! MER INFORMATION Välkommen att kontakta Anna via studentsamarbete@jonkopingenergi.se för mer information. På grund av att vi är en samhällskritisk verksamhet kommer du att säkerhetsprövas. Processen för säkerhetsprövningen kan ta upp till 5 veckor, det är därför viktigt att du ansöker i god tid innan din LIA-period börjar.
Vill du göra praktik i en samhällsbärande organisation som drivs av hållbarhet och utveckling? Vi på Jönköping Energi vill vara en god kraft för ett gott samhälle och vi brinner för att utvecklas med Jönköping. Varje dag bidrar vi till ett hållbart samhälle och en hållbar miljö, vilket kräver öppenhet, innovation och ständig utveckling. Vi erbjuder löpande LIA-platser till studenter som läser elkraftsingenjör. OBS! Vårens LIA-platser är tillsatta. Vi tar emot igen till hösten och börjar planeringen i sommar. Under din LIA-period kommer du att få se flera olika delar av vår verksamhet inom elnät och du kommer att kompetensutvecklas genom mötet med våra grymma kollegor. Då Jönköping Energi även har verksamhet inom ladd, sol, fjärrvärme och fiber kommer du även få en viss kunskap inom energisektorn som helhet. Vi kan erbjuda dig en möjlighet att samarbete med ett företag som jobbar med några av samhällets viktigaste funktioner och infrastrukturer. Jönköping Energi är inne i en expansiv fas med utveckling och många spännande utmaningar framför sig. VI SÖKER DIG SOM läser elkraftsingenjör, antingen genom en eftergymnasial yrkesutbildning eller på högskola/universitet. Vi tror att det är bra att få in en mix av utbildningsnivåer. Nyfiken och engagerad är två viktiga egenskaper för oss! Beskriv gärna hur dessa egenskaper visar sig hos dig! MER INFORMATION Välkommen att kontakta Anna via studentsamarbete@jonkopingenergi.se för mer information. På grund av att vi är en samhällskritisk verksamhet kommer du att säkerhetsprövas. Processen för säkerhetsprövningen kan ta några veckor, det är därför viktigt att du ansöker i god tid innan din LIA-period börjar.
Omegapoint är ett konsultföretag som skapar framtidens säkra digitala tjänster. Nu har du chansen att skriva din masteruppsats hos oss och få handledning samt tillgång till våra experters gedigna kompetens. Är du den vi söker? Vi söker främst dig som studerar datavetenskap, datateknik, informationsteknik, informationsteknologi, interaktion & design, teknisk fysik eller motsvarande vid Umeå universitet och ska skriva din magister-/masteruppsats under höstterminen 2026. Vi ser gärna att ditt arbete innehåller en betydande del systemutveckling och helst har något inslag av säkerhet, men är intresserade av alla möjliga ämnen inom IT och datavetenskap. Vilka tekniker eller frågeställningar har engagerat dig senaste tiden? Vi bollar gärna idéer med dig! Vad erbjuder Omegapoint? Du som skriver ditt exjobb hos oss får handledning av en av våra konsulter. Tillsammans med andra exjobbare sitter du på vårt kontor och har tillgång till våra resurser. Du är även välkommen att delta på våra kompetensutvecklingsaktiviteter och sociala tillställningar. Blir du sedan kvar på Omegapoint efter arbetet vågar vi lova dig en arbetsplats där du kommer att trivas, utvecklas och ges alla möjligheter till en ljus framtid. Ansök nu Använd formuläret nedan för att ansöka om en plats. Skriv lite om din bakgrund, vad du brinner för, berätta om dina idéer och vilka ämnen du är intresserad av. Vi rekryterar studenter löpande och har ett begränsat antal platser varje termin. Varmt välkommen med din ansökan! 💚
Vi är glada att du är intresserad av att skriva ditt exjobb eller göra din praktik/LIA hos oss på Vitec Software Group! Vi tar emot exjobbare/praktikanter inom områden där vi tror att vi har kompetens och möjlighet att bidra med en givande tid för dig som student hos oss. Vi prioriterar personer som har exjobb/praktik/LIA som en del av din utbildning på universitet, högskola eller yrkeshögskola. Här kan du läsa mer om Lise och Efraim som skrivit sitt exjobb eller gjort sin praktik hos oss under sin utbildning. Vi tar emot ansökningar om exjobb med egna förslag eller ansökningar där du är öppen för förslag från oss. Hur ansöker du om exjobb/praktik/LIA? Anmäl ditt intresse via länken ovan, svara på frågorna och bifoga CV. Frågorna täcker: * Kort beskrivning av innehållet i din utbildning * Om du är intresserad av exjobb eller praktik/LIA * Vilket område du är intresserad av vid exjobb * Tidsperiod för exjobb/praktik/LIA Om det finns en matchning mellan dina och våra behov och önskemål kommer vi att kontakta dig för en intervju. Vi ser fram emot att höra från dig!
Är du student och letar efter en möjlighet att göra ditt examensarbete eller praktik hos oss? Vi välkomnar alltid engagerade och nyfikna studenter som vill omsätta sina kunskaper i praktiken och få värdefull erfarenhet i arbetslivet. Vi har alltid en öppen ansökan för praktik och exjobb där du kan skicka in din spontanansökan. Eftersom vi går igenom ansökningarna löpande och utifrån verksamhetens behov, kan vi tyvärr inte garantera att det alltid finns aktuella möjligheter. Detta innebär också att du inte kan räkna med en omedelbar återkoppling på din ansökan. När vi har specifika behov kommer vi att lägga ut riktade annonser, och då är du varmt välkommen att söka dem också. Vad vi söker Vi letar efter studenter från eftergymnasiala utbildningar inom relevanta områden som kan bidra med nya perspektiv och idéer. Ange gärna i din ansökan vilket område du är intresserad av, vilken period du söker praktik eller exjobb för, samt eventuella krav från din utbildning. Så här ansöker du Skicka in din ansökan med CV, personligt brev och information om dina önskemål och förutsättningar. Vi sparar din ansökan och hör av oss om en passande möjlighet dyker upp. Vi ser fram emot att ta del av din ansökan och kanske få möjligheten att arbeta tillsammans i framtiden!