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Graduation Assignment: Dynamic Roster Planning in Healthcare with LLM-Supported Problem Modelling
WO/HBO - Bachelor or Master ᐧ AI / ML / Computer Science or similar ᐧ 5–10 months starting in August 2026 or later ᐧ Hybrid (remote/office) ᐧ € 400 - € 700 / month

Who we are
Praevi is a startup founded by two experienced entrepreneurs. It's born from the daily frustrations that people in healthcare have with the existing work rostering processes. Poor work-rostering drives up sick-leave and staff turnover while at the same time the demand is rising due to an aging population. Existing solutions don't solve this, so we started Praevi to target this from a different angle.
Vincent van den Tol (technical co-founder, computer science background) is responsible for the product and technology. Léon Janssen (commercial co-founder) leads the relationships with healthcare organizations and commercial growth.
We're now looking for an AI/ML or computer science graduate to join our growing team and improve the personal alignment and flexiblity of automated planning processes. Why you should consider this assignment
Let's face it, everybody want to do something with AI and this is probably not the only assignment you will read. But while most assignments are focused on saving costs, identifying risks or finding new business opportunities, we offer the possibility to have a direct impact on society by helping healthcare organizations, nurses and physicians to keep healthcare accessible.

Context
Dutch healthcare is under strain: demand is growing while staffing falls short. Rostering ("who works when") is critical to using hours efficiently and keep people happy and energized. Instead, it's often a major source of frustration due to inflexibility and lack of autonomy. Many optimization algorithms exist, but we see that a lot of healthcare teams don't use them, or keep a manual process on the side. We believe this is because of:
  • Formal mathematical solvers require correct and complete problem modelling, which is hard and time-consuming to do, especially in a domain as dynamic as healthcare.
  • Complex systems often act as a "black box" for people, and therefor lack the feeling of control, trust and transparency.
As a result, qualified staff spend days per month on manual planning, changes require constant back-and-forth, louder voices win, and rules depend entirely on the individual planner — hurting job satisfaction and retention.

Our approach
Rather than optimizing existing solutions by a few percent, we believe we can have more impact by first making automatic rostering accessible to everyone in healthcare. This requires us to think differently:
  • It should be easy to set up an automated planning model from existing processes
  • Applying changes or testing new scenarios to existing rosters should be done fast .
  • Solutions should be transparent and explainable ("why wasn't she assigned to that shift?")
  • A solution should learn people's (personal) preferences to produce better-aligned rosters
We see an opportunity to combine LLMs with proven ML algorithms, modern interfaces, and agent-based execution to implement these requirements.
Our application is already running inside a first hospital in The Netherlands and we are in the process of setting up pilots with more organizations. We have set the groundwork, and your work will contribute to expanding this foundation into our long-term vision of a smart, flexible and user-friendly planning solution.

The assignment
We currently have an open position to help us improve our AI agents to deal with the questions above, and we are happy to share our ideas if you are interested.
For your assignment you will work in a small but experienced team. You will have freedom to work how and where you like, you can (optionally) participate in related developments if that helps your assignment, and work with very direct lines and regular face-to-face moments. To research and test your solutions we will provide you with technical tools & infrastructure and connect you with healthcare providers in our network.

Technology
As a privacy-focused company in a sensitive sector, we focus on open-source frameworks and 100% European (cloud) infrastructure. Your research and solutions will need to work within a Python based environment, using mainly open source technologies and frameworks. You will get access to AI tools for coding assistent and analysis.

Practical details
Level: [WO / HBO — Bachelor or Master] Background: AI / ML / Computer Science or similar Required skills: Python; affinity with optimization/MILP and LLMs is a plus Language: English or Dutch (Dutch is preferred for communication with healthcare professionals) Duration & time frame: 5-10 months, starting in August 2026 or later Compensation: € 400 - € 700 / month Location & work mode: Hybrid (our office is in Haarlem, 10 min walk from Central station) Deliverables: Besides research we are looking for a working and tested prototype and documentation Supervision: Day-to-day support and technical guidance from Vincent (technical co-founder) How to apply: Reach out to Vincent on vincent@praevi.nl or +31641298581 Website: https://praevi.nl/