Practical experience
Landing a job takes more than talent, it takes action. Demola gives you the chance to tackle real challenges, collaborate with others, and build proof of what you can do even when things are uncertain. By actively participating in a Demola project, you can turn your skills into competencies and concrete experience.
Partnered with Innovative Companies
Our platform, created together with leading companies and professors, connects you to real-world innovation projects. You’ll work as part of a team on an innovation and development project, guided by Demola’s expert facilitators. Participating in a project will give you practical experience and the confidence to deal with complexity in any job.
At Demola, innovation projects are built around real-world project topics co-designed with companies and public-sector organisations. If selected, you'll join an innovation team working to develop meaningful solutions that respond to validated needs.
Our projects tackle complex, relevant problems grounded in industry and societal needs. You'll work in a team to design and demo solutions, benefiting from the insights and findings of previous Demola projects. Most Demola projects last around two months, but the exact duration depends on the programme they are part of.
Each team benefits from expert guidance through coaching sessions and facilitated community events. Demola's facilitators bring years of experience in innovation and adapt their support to match your ambition and commitment. You are expected to take an active role, both individually and as a team.
You'll co-create with other teams and Demola's experts, and validate and test your solutions with real stakeholders to continuously refine your approach. High-performing teams gain access to a network of professionals from Demola's industry partners for further input and exposure.
You'll build in-demand, career-ready skills in creative problem-solving, AI-assisted innovation, project management, international collaboration, and resilience - all while working in an environment that mirrors real innovation practices.
Your team owns the outcomes created during the project. Use them in job interviews, your portfolio, a Master's thesis, or even as the foundation of a startup. In some cases, your work may also earn you academic credit (ECTS) if you are a student.
The impact
Ready to turn your skills into competence? Our claims are backed by feedback from our community of participants and alumni.
Strongly agree (49.3%)
Agree (43.6%)
Neither agree or disagree (5.3%)
Disagree (1.3%)
Strongly disagree (0.5%)
2 272 people took part in the survey.
New skills
65.6%
Valuable work experience
63.9%
New friends
51.5%
Startup ideas
34.4%
International teamwork experience
30.5%
Industry contacts
23.9%
Self-esteem
23.9%
New motivation to study
22.8%
Confidence in career choices
20.3%
Researcher contacts
17.6%
Better position in the labor market
13.5%
Other
1.5%
Based on Q4/2022 survey
If you’re currently studying at or near one of our Demola locations or partner universities, you’re welcome to join a Demola project.
In our partner cities, the projects are also open to graduates and professionals who are exploring new career paths or interested in co-creating and launching startups in the future.
Many alumni have participated in more than one Demola project – the current record is seven. For active alumni, we facilitate further development in their academic studies or future careers:
Explore or fine-tune your Master’s thesis topic through a Demola project and find a potential industry collaborator for your Master’s thesis after the project.
Develop an expert profile that highlights your project contributions, as well as innovation and interpersonal skills. Top-performing participants will have their expert profiles shared and recommended within Demola’s partner network, verifying their skills and proactive mindset.
Build on your project results, connect with like-minded teammates, and use your entrepreneurial mindset to lay the groundwork for a startup.
Get onboarded with Demola
Create a Demola profile.
Browse project in your location and apply to those that interest you.
If you get selected, confirm your seat and start the teamwork.
Apply to one or more projects today!
Jyväskylä
Value Creators of Tomorrow
Finland's defence has long relied on dispersed operations, where stretches of ordinary public road can be turned into temporary airstrips for aircraft. Most of the time these roads carry everyday traffic, suffer from frost heave, snow-plough damage and potholes, and are maintained to civilian standards. When they are needed in a real crisis, however, the surface must be flawless – at short notice, possibly in the middle of winter, at night, or after deliberate damage. New mobile technologies, such as equipment that produces hot asphalt on site and enables permanent repairs even in freezing conditions, open up new possibilities for closing the gap between everyday maintenance and crisis readiness. This project explores how the condition of a road airstrip can be ensured so that it is landing-ready exactly when it is needed. What does "landing-ready" actually mean for a road surface, and how could its condition be monitored continuously instead of inspected only before use? How might sensors, drones, vehicle-based scanning or AI image analysis detect damage in real time? How fast could a stretch of road be inspected, repaired and cleared in the harshest conditions, and what mobile capacity, logistics and people would it require? How might civilian road-maintenance companies become part of national preparedness, and could the same capability create everyday value for rescue services, emergency helicopter landings or disaster response? The repair technology already exists – the missing piece is the future system around it. This project aims to create a future vision of road airstrip readiness in the form of scenarios, concept designs and service models, combining engineering, security, logistics, service design and business perspectives.
Apply by 11 Oct
Jyväskylä
Future of Work
Software developed in regulated industries — defence, medical devices, critical infrastructure — must satisfy requirements that change, sometimes abruptly, as standards are updated or new legislation takes effect. A long-running development project can find itself out of compliance mid-build, with the cost of correction scaling with how late the divergence is caught. The project asks: how can agentic AI be designed to hold regulatory and standards context as a live constraint throughout a development project, and how should it respond when that context changes? The challenge has two parts — encoding the relevant regulatory requirements in a form the agent can reason over, and designing the agent's behaviour when a regulatory change conflicts with work already done or in progress. The team would select a specific regulatory domain (for example, IEC 62443 for industrial cybersecurity or IEC 62304 for medical device software) and prototype an agentic system that monitors development artefacts against the selected standard, flags deviations, and generates concrete remediation suggestions. The prototype would also demonstrate a simulated regulatory update scenario and show how the agent propagates the change through its compliance model. The deliverable is a working prototype of a regulation-aware development agent, with a documented evaluation on a representative set of compliance scenarios and a design pattern for integrating live regulatory context into long-running software projects.
Apply by 11 Oct
Jyväskylä
Future of Work
Background Regular patient screenings in public healthcare are, in principle, one of the more predictable parts of the system: people of a certain age are invited on a known schedule, and population data for a wellbeing services county is generally available. In practice, however, workforce planning for the units running these screenings tends to be reactive rather than forecast-driven, and the flow of patients into and through the screening process over time is rarely modeled explicitly. At the same time, the demographics of wellbeing services counties are shifting, which changes both how many people will be eligible for screenings and how that number will develop in the coming years. Challenge How can we forecast how many people will take part in regular patient screenings, and translate that into the workforce needed for the specific unit running them? The project will look at how to model the flow of patients into screenings over time, using the fact that screening demand is largely predictable: eligible age groups are known, county population and demographic data is available, and historical no-show rates can be factored in. It will also explore how changing demographics — population growth, aging, or migration within a county — should be built into the forecast rather than treated as a fixed baseline, and what a useful forecast should show, including scenario views (e.g. tied to regional population growth projections), the workforce implications of different scenarios, a simple way to weigh the cost/benefit of adding staff against the cost of a growing queue, and how the system should behave once demand or a queue approaches zero. Goal 1. Build a forecasting model that estimates screening participation from population age structure, invitation schedules, and historical no-show rates. 2. Model the flow of patients into and through screenings over time to estimate the workforce needed for a specific unit. 3. Incorporate demographic change and population growth scenarios into the forecast, rather than assuming a static population. 4. Explore a simple recruitment ROI view that weighs the cost of adding workforce against the cost of a growing patient queue, and consider what happens once a queue approaches zero. 5. Explore how forecasted queue status could be communicated back to patients, so they have a better sense of what to expect. Scope The project is not aimed at a production forecasting or workforce-planning system, but at validating whether screening participation and the resulting workforce needs can be usefully forecast from available population and historical data. The scope is limited to one or two representative screening programs with simplified demographic and queue data, so most effort goes into testing the forecasting and scenario-modeling approach rather than building a fully integrated planning tool.
Apply by 11 Oct
Jyväskylä
Human Beings in the Modern World
Background Patients in public healthcare often struggle to understand what will happen to them during their care. From first contact through diagnosis and treatment, the pathway a patient follows is, in most cases, fairly standard — but it is not visible or explained to the patient. This leaves people uncertain about what comes next, why certain steps happen, and how long the process will take, adding unnecessary stress on top of an already difficult situation. Challenge How can a patient pathway be automatically created and visualized as soon as a patient enters public healthcare services, and how could it be updated as the diagnosis is confirmed or the care progresses? Wellbeing services counties already publish large amounts of information — guidelines, condition descriptions, treatment options, rehabilitation advice — but this information is scattered, hard to find, and sometimes hard to understand. The project will investigate how such information could be attached to the right point in a patient's pathway, so individuals can easily learn what is happening next and why. ## Goal 1. Design a method to automatically generate a visual patient pathway from patient records and diagnosis data. 2. Enable the pathway to update dynamically as the diagnosis is refined or the care progresses. 3. Link relevant existing information (guidelines, condition descriptions, treatments, rehabilitation) to the corresponding steps of the pathway, so patients can understand what is happening and why. 4. Build a simple "pathway generator" prototype that produces a patient pathway based on patient records, diagnosis, and how the care is progressing. Scope The project is not aimed at a production-ready patient-facing application or full integration with real electronic health records, but at validating whether a patient pathway can be automatically generated, visualized, and kept up to date, and whether existing county information can be meaningfully attached to it. The scope is limited to one or two representative care pathways (e.g. common diagnoses) with simplified, illustrative data, so most effort goes into testing the generation and information-linking approach rather than building a polished end-user product. Expected Outcomes - A working prototype of a "pathway generator" that visualizes a patient's care journey based on records, diagnosis, and care progression. - A method for linking existing wellbeing services county information (guidelines, condition and treatment information, rehabilitation) to the relevant steps of the pathway. - Findings on how well the pathway can be kept up to date automatically as diagnosis or treatment changes, and where manual input or oversight is still needed.
Apply by 11 Oct
Jyväskylä
Future of Work
Background Work shift planning in the public healthcare sector is largely a manual, recurring administrative burden. Shift planners spend significant time each planning cycle building rosters by hand, reconciling employee requests, absence rules, competence requirements, and labor agreements — often in spreadsheets or legacy scheduling tools that offer little intelligence beyond basic rule-checking. In Finland's wellbeing services counties (hyvinvointialueet), this work is repeated across dozens of units and thousands of employees, with limited standardization between them. At the same time, generative and predictive AI have matured enough to handle constraint-heavy optimization problems, interpret free-form employee requests, and flag risk patterns in structured data — capabilities that map directly onto the pain points of shift scheduling, but that have not yet been applied systematically in this domain. Challenge How can a simple, practical AI-powered shift scheduler be built for use in the public healthcare sector — one that takes over as much of the routine planning work as possible while keeping humans in control of the outcome? The project will investigate and test AI use across the following areas, among others: - Employee requests: capturing free-form preferences and constraints (e.g. "I need to be home by 16:00 on Tuesdays," "I want as many weekend shifts as possible") and translating them into a schedule the AI can optimize against. - Workload and burnout risk: tracking workload over longer periods (not just single shifts or weeks) and alerting management when an employee shows signs of being at risk of burnout. - Feedback loop: giving employees a channel to tell management how they feel about the resulting schedules and what should change, and feeding that signal back into future planning rounds. - Human-in-the-loop design: structuring the system so that the AI produces ready-made schedule proposals, and the only action left for humans is to approve or reject them — and testing how far that ambition can realistically be pushed. Goal 1. Design and test an AI-based scheduling engine that turns employee requests and constraints into draft shift plans for a healthcare work unit. 2. Build a workload-tracking mechanism that monitors employees over longer periods and raises early alerts for burnout risk. 3. Create a feedback loop through which employees can report on schedule quality and fairness, and route that input back to management and into future scheduling rounds. 4. Determine how close the system can get to a fully AI-generated, human-approved-only workflow — and identify where human judgment is still required. Scope The project is not aimed at a commercializable or production-ready scheduling product, but at validating whether AI can meaningfully take over the core mechanics of shift scheduling in a public healthcare setting. The scope is limited to one or two representative work units (e.g. a ward or care team) with a simplified but realistic rule set, so that most effort goes into testing and evaluating the AI's scheduling, risk-detection, and feedback-handling capabilities rather than building a full-featured, organization-wide system. Expected Outcomes - A working prototype of an AI shift scheduler that generates draft rosters from employee requests and constraints for a pilot work unit. - A workload-risk alerting mechanism based on workload tracked over longer periods. - A functioning feedback loop between employees and management on schedule quality. - Findings on how much of the scheduling process (proposal generation, risk detection, and iteration) can be handled by AI with humans only approving or rejecting outcomes, and where human oversight remains necessary.
Apply by 11 Oct
Jyväskylä
Future of Work
Background Generative AI use in software development is already mainstream: AI-assisted code generation, testing, and documentation are established practices, largely because vast amounts of training data (code repositories, documentation, forum discussions) are available. In hardware development — circuit design, component selection, PCB layout, mechanical design, HW testing — the situation is different: publicly available, structured training data is a fraction of what exists for software, and the toolchain (EDA software, datasheets, simulators) is more fragmented and less "open" to AI. As a result, AI's role in embedded system (hardware + software) development has so far remained far more modest than in pure software development, even though there is significant potential to accelerate the entire development process. Challenge How can AI be leveraged as broadly and practically as possible in an embedded system development project — from concept to working prototype? The project will investigate and test AI use across the following stages, among others: - Design and architecture: system-level design, block diagrams, interface definitions with AI assistance. - Component selection: datasheet interpretation, comparison of alternative components, availability/cost optimization, lifecycle risk identification. - Circuit design and PCB layout: AI-assisted schematic capture, layout recommendations, noise/interference checks. - Embedded software: drivers, protocols, firmware generation and review. - Testing and validation: can AI replace or supplement physical testing (simulation, automated test-case generation, result interpretation) — and where are the limits. These findings will be compiled into a practical "HW Developer's Handbook" that provides concrete guidance and tool recommendations on how to (and how not to) leverage AI at each stage of a hardware project. Goal 1. Map out and test AI tools and methods at different stages of embedded system development. 2. Identify where AI genuinely delivers speed or quality gains, and where its use is still unreliable or risky. 3. Produce a repeatable handbook on leveraging AI in hardware development, shareable with other teams and projects. 4. Test the handbook with a demo - create a simple hardware project where you apply the handbook guidelines. Scope The project is not aimed at a commercializable product, but at validating the process and method (the handbook) through one or two concrete demo devices. Device functionality is kept simple enough that most of the time goes into documenting and evaluating how AI was used, not into polishing the device itself. Expected Outcomes - A working demo device (home automation node), built using the handbook's methods. - An "HW Developer's AI Handbook": a step-by-step guide, tool recommendations, examples, and benefit/risk findings - A summary of where AI can replace or supplement traditional testing, and where it cannot.
Apply by 11 Oct
See how Demola has helped students around the world succeed in their careers and personal lives.
Master’s Degree student in Research and Innovation in Higher Education
Excellent
20/04/2023
“I enjoy connecting with other people who are not from the same area and field, and creating new things. I think Demola is a very great platform and, since I’m interested in education, I think this is a great idea to help students and other people to learn in a different way and to be closer to the work life.”
Master’s Student in Public and Global Health
Excellent
20/04/2023
“I joined Demola for the third time already, since I enjoy how my personal skills are developing here and I love the feeling of freedom of thoughts, to be able to think, feel and express anything.”
Master’s Student in Sustainable Digitalisation
Excellent
20/04/2023
“I wanted more experience in interdisciplinary project work and real-life, tangible development of solutions with external stakeholders. I also wanted to practice what the design process is like in the real world and how to present novel ideas in an attractive, business- oriented way. And Demola delivered!”
Master’s Degree Student in Engineering and Technology
Excellent
10/06/2022
“In Demola we focused on creating solutions that can and should be applicable and should happen in real life, concrete and implementable solutions that can have an impact. Those type of solutions that go way beyond than just making a new website or a nice social media page.”
Master of Business
Excellent
17/04/2023
“If you have successfully completed the Demola project, it is proof of your passion and that your mindset is inevitably customer-oriented. Demola is definitely an experience I can recommend to all students.”
Get the information you need with our comprehensive list of FAQs below:
Where can I stay up-to-date on the new projects and events?
Will I get my Demola certificate automatically after finishing the project?
I got accepted! What's next? How can I find my teammates?
What do the project's final results look like? What are the evaluation formats?
How much time should I prepare for Demola project?
Do you have any questions about Demola? See our help center
Join Demola and work on career-relevant projects with global impact.
