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We are seeking a highly motivated doctoral researcher to join the What Makes Us Human? Identifying, Evaluating and Modelling Specialist AI Competence (HUMAIN) project, investigating how large language models (LLMs) approximate, extend, or diverge from human expertise across domains. The starting date is negotiable but should be no later than November 1, 2026. The position is fully funded until August 31, 2030 (46–48 months, depending on the starting date). About the project As LLMs become increasingly embedded in education, work, and everyday life, understanding their relationship to human competence is both scientifically and societally urgent. Existing benchmarks largely measure accuracy or linguistic fluency, but overlook the tacit, contextual, and ethical dimensions that define real-world expertise. HUMAIN addresses this gap by developing the online Competence Imitation Game (COMIG), a novel, experimentally validated framework that allows domain experts to interrogate and evaluate human- and LLM-generated responses through structured question-answer-evaluation conversations. Combining computational and interpretive methods, the project will generate reproducible, cross-domain datasets of paired human–AI responses, analysed through embedding-based similarity measures, feature extraction, LLM-as-evaluator approaches, and qualitative interpretation. Domain experts are involved from the outset, helping to develop practitioner-informed metrics for imitation quality, contextual alignment, and pragmatic fit. Empirically, HUMAIN will conduct seven COMIG studies spanning everyday life, education, and work, engaging over 900 participants internationally. The project will also evaluate adaptation techniques, including prompt calibration, retrieval-augmented generation, and fine-tuning, to examine how LLMs can better align with domain-specific reasoning. For more information on COMIG, please see the article ”Introducing the Competence Imitation Game: A Research and Teaching Tool for Context-Specific AI Literacy” (https://ceur-ws.org/Vol-4138/paper3.pdf).  About the position You will support the further development of COMIG and contribute to empirical data collection, analysis, and reporting. You will be a part of the Research group on AI in Society and Education (RAISE) and work closely with Associate Professor Linda Mannila, PhD Otto Segersven and a postdoctoral researcher to be recruited. Who we are looking for We welcome applicants with a background in computer science, data science, language technology, or a related field. Applicants with, for example, a master's degree in social sciences combined with a strong background in data science, computational methods, or computer science are equally eligible. The position requires: Strong background in research methodology, including the design and implementation of empirical studies with human participants. Strong analysis skills, including both quantitative and qualitative methods. Strong background in computational methods, especially for working with text data. Experience with mixed-methods research (quantitative and qualitative). Excellent English skills. Knowledge of Finnish and/or Swedish, considered a merit, as some studies will preliminarily be conducted in Finnish contexts. LLM expertise, including prompt calibration, retrieval-augmented generation, and fine-tuning, considered a merit. In addition, according to the system of the University of Helsinki, all doctoral researchers belong to a doctoral programme. The successful candidate should apply to Doctoral Programme in Science and obtain the study right within the trial period of six months of their employment contract. Please check the admission periods and eligibility criteria at https://www.helsinki.fi/en/admissions-and-education/apply-doctoral-programmes/how-apply-doctoral-programmes. We offer A world class academic community that promotes diversity, international engagement and equality. We encourage all qualified applicants from diverse backgrounds to apply for our positions A starting salary of approximately 2800 euros/month Occupational health care and other social benefits Support for internationally recruited employees with their transition to work and live in Finland A chance to live in Helsinki and Finland renowned for their high quality of life, services, and work-life balance Application instructions Applications are to be submitted through the University of Helsinki´s recruitment system and include the following documents: CV  A motivation letter (max 2 pages). Please also include a brief description of your research ideas relevant to the project, for example, how you would design the empirical studies. We are interested in your own thinking about how to approach the methodological and analytical challenges the project raises. Contact information of two referees to provide reference letters upon request  The deadline for submitting the application is August 16, 2026 (at 23:59 UTC + 3). External applicants, please submit your application via the Apply Now button. Internal applicants, who have a helsinki.fi username and a valid employment/grant/visitor contract, please submit your application via the Employee login button. Additional information For more information on the position and the Research group on AI in Society and Education, please contact Associate Professor Linda Mannila (linda.mannila@helsinki.fi).  With technical questions related to the recruitment system, please contact recruitment@helsinki.fi. *** The Department of Computer Science at the University of Helsinkin is a leading unit in Finland in its area and responsible for the teaching and research in computer science at the University of Helsinki. The main research fields at the department are artificial intelligence, big data frameworks, bioinformatics, data analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and database systems. The department has extensive international collaboration with companies and universities. Within teaching, the department’s professors and staff are in charge of the Bachelor’s, Master’s, and Doctoral Programmes in Computer Science, as well as the separate Master’s Programme in Data Science, in which other departments also participate. 

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. This role supports the Future Ship and System Design (FSSD) Programme, which aims to accelerate the decarbonisation and digitalisation of Singapore’s maritime sector. The project focuses on developing planning methods to support the electrification of harbour craft fleets, using real-world operational data to derive charging demand profiles and infrastructure recommendations. As part of this work, we are seeking a Research Fellow to lead the development of a user-friendly GUI tool that will enable stakeholders to interact with planning models and visualise simulation outputs. The RF will also support increasing coordination between different projects as the number of case studies and incoming datasets continues to grow. Key Responsibilities: Design and develop a GUI tool to make model inputs/outputs accessible to end-users Collaborate with project leads and researchers to ensure technical accuracy of the interface Support integration of simulation outputs across Projects 3-1 and 3-2 Manage and process large sets of operational data for case study analysis Contribute expertise in power grid optimisation to enhance planning models Participate in stakeholder engagements and respond to evolving technical needs Job Requirements: A strong background in power systems, energy modelling, or grid optimisation Proficiency in GUI development (e.g. using Python Dash, Tkinter, or web-based frameworks) Experience working with data-driven simulation and model validation in power grids (Ph.D level is mandatory) Ability to work independently and collaboratively in a fast-paced, multi-stakeholder environment Prior exposure to maritime or transport-related electrification studies is a plus

Schemes of Service: Research Division: Engineering Employment Type: Fixed Term As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. To provide technical and operational leadership in the development, execution, and translation of sustainable geomaterials for land reclamation under an industry-collaborative research programme, working under the strategic direction of the Principal Investigator (PI) to ensure successful delivery from laboratory research to pilot and field implementation. Key responsibilities include: Support the PI in the planning, execution, and delivery of the research project, including leading day-to-day technical activities to ensure milestones and deliverables are achieved within timeline and budget. Provide technical leadership in the development of carbonation-engineered geomaterials (e.g. demolition fines, incineration bottom ash) for reclamation applications, aligned with the research direction set by the PI. Design and oversee experimental programmes, including laboratory testing, pilot-scale trials, and field validation, ensuring technical rigour and relevance to industry application. Translate research findings into engineering solutions, including material specifications, performance frameworks, and implementation methodologies suitable for industry adoption. Lead the scale-up of laboratory processes (e.g. aqueous carbonation) to prototype and pilot systems, including system design, optimisation, and operational considerations. Supervise and mentor research staff (RFs, REs, RAs), including task allocation, technical guidance, and monitoring of progress to ensure effective team delivery. Coordinate with industry partners and vendors on technical matters, including trials, material supply, equipment development, and site implementation. Identify and resolve complex technical challenges related to material variability, process scalability, and environmental compliance. Oversee environmental performance assessment, including leachate testing and alignment with regulatory requirements for reclamation use. Prepare and contribute to technical reports, journal publications, and presentations for stakeholders, funding agencies, and dissemination. Support the PI in identifying new research opportunities, contributing to proposal development, and strengthening industry engagement. Co-supervise undergraduate, Industrial Master and Doctoral students in alignment with project objectives. Ensure compliance with Work, Safety and Health (WSH) regulations, including maintaining risk assessments and safe laboratory and field practices. Job Requirements PhD in Civil Engineering, Geotechnical Engineering, or a related discipline, with at least 10 years of relevant industry or research experience; or Bachelor’s or Master’s degree with at least 15 years of relevant industry or research experience. Demonstrated experience in leading technical components of research or engineering projects and supervising junior staff. Experience in experimental programme design, laboratory and/or field testing, and data-driven engineering evaluation. Familiarity with material treatment or processing methods (e.g. stabilisation, carbonation, or related technologies) will be advantageous. Experience in industry collaboration, pilot-scale implementation, or technology translation will be advantageous. Experience in reclamation or ground improvement will be advantageous. Knowledge of environmental assessment and leachate testing for construction materials will be an advantage. Key Competencies Strong technical leadership and mentoring capability Advanced problem-solving ability in complex engineering environments Ability to translate research into practical and scalable engineering solutions Strong project coordination and delivery skills Effective communication with multidisciplinary teams and industry stakeholders High level of ownership, initiative, and accountability Adaptability across laboratory, pilot-scale, and field environments