Research Engineer in AI-driven Social Simulations

Sveriges Lantbruksuniversitet (SLU); Uppsala kommun; Hybrid; SEK 390,600 - 613,800; Full time

Job summary

Sveriges Lantbruksuniversitet (SLU) is seeking a Research Engineer in Uppsala to develop AI-driven social simulations. You'll work in a dynamic research environment investigating public policy impacts through large-scale simulations built on AI technologies.

The role involves building simulation frameworks, running AI models, and co-authoring publications. Candidates should have a strong background in computer science or engineering and demonstrate technical ability through practical projects or contributions.

Qualifications

• A Master or engineering degree is sufficient; a PhD is welcome but not required.
• Demonstrated technical ability in building deployable AI projects.
• Experience with deep reinforcement learning or multi-agent systems.

Responsibilities

• Building LLM-agent infrastructure for simulations.
• Running large-scale Monte Carlo simulations on GPU-enabled HPC clusters.
• Publishing and co-authoring scientific publications related to the project.

Skills

Strong programming skills, especially in Python

Experience with LLMs and AI systems

Experience with high-performance computing

Ability to work independently

Interest in nature conservation and environmental policy

Education

Degree in computer science, engineering, physics, applied mathematics, or related field

Tools

Machine learning/deep learning frameworks

Version control systems

Job description

Research Engineer in AI-driven Social Simulations

Kommun: Uppsala

The Swedish University of Agricultural Sciences (SLU) is one of Northern Europe largest academic hubs for ecological research and offers a dynamic and excellent research environment with modern infrastructure. The Department of Ecology has about 150 employees, of whom around 40 work at the Grimsö Research Station in Bergslagen. Together, the SLU Ecology Centre and Grimsö Research Station conduct research on sustainable agriculture and forestry, plant protection, nature conservation, and wildlife management, and provide scientific knowledge to inform Sweden and Europe's environmental policies.

About the position

Project

Can we foresee the outcomes of public policies, political choices and other decisions? We are finding out by developing serious games and running them using AI.

We are building large-scale simulations of societal processes (environmental negotiations, nature conservation policies, and hybrid-threat scenarios) in which every actor is an autonomous AI agent powered by Large Language Models (LLMs). These agents simulate real-world stakeholders, from government ministers to interest groups, and interact through natural language in complex strategic settings. We then run thousands of iterations to map the distribution of outcomes.

This effort is part of a new research program Articulating Complexity ( https://www.slu.se/articulating-complexity/ ) hosted at the Swedish University of Agricultural Sciences (SLU) and led by Guillaume Chapron (Docent). It is funded by grants from the Swedish Research Council (VR), the Swedish Foundation for Strategic Environmental Research (Mistra), and the Swedish Research Council for Sustainable Development (FORMAS).

Our goal is to build a novel methodology at the intersection of AI, ecology, political science, and complex system analysis that may change how policies are designed and stress-tested and how governments prepare for crises. We are looking for an ambitious research engineer to join this effort.

Tasks and duties

You will be the person who makes the simulations actually run. Concretely, this means:

• Building the LLM‑agent infrastructure (from an existing working implementation). Each agent has a multi‑tier memory and an affect state that modifies both prompts and available actions, and a strategic reasoning layer that tracks reputation, commitments, and mental models of other agents.
• Extending and maintaining the multi‑agent simulation framework. A simulation runs dozens of concurrent LLM instances, communicating through structured message‑passing protocols. You will develop and maintain this framework, including the validation methodology.
• Running large‑scale Monte Carlo simulations at scale on GPU‑enabled HPC clusters (e.g., NAISS).
• Publishing and dissemination of the project results. You will co‑author scientific publications from the project. The research will produce papers at the intersection of computational social science, ecology, security studies, and AI.
• A degree in computer science, engineering, physics, applied mathematics, or a related quantitative field. A Master or engineering degree is sufficient; a PhD is welcome but not required.
• Strong programming skills, especially in Python, and sufficient understanding of machine learning/deep learning to work effectively with modern generative AI systems. Ability to assess and incorporate cutting‑edge generative AI developments into the project.
• Experience with software engineering practices such as version control, testing, documentation, modular design, and reproducible workflows.
• Ability to work independently, debug complex systems under time pressure, and take ownership of technical infrastructure.
• Experience with LLMs, including programmatic use through APIs and local deployment. The candidate should be able to select, configure, adapt, and critically evaluate LLMs for research applications.
• Experience with deep reinforcement learning, agent‑based modelling, or multi‑agent systems, including the design of simulation environments, interacting agents, decision rules, feedback mechanisms, and scenario‑based analyses.
• Experience with high‑performance computing: batch job submission, GPU workflows, and managing large‑scale computational experiments.
• Interest in the application domain: nature conservation, environmental policy, political science, geopolitics and security studies, complex adaptive systems. You do not need a formal background in these fields, but you should find them interesting.

Assessment criteria

Applications will be assessed on the following:

• Demonstrated technical ability. We care more about what you have built, than about what courses you have taken. A GitHub portfolio, a deployed system, a well‑documented side project, or a track record of solving hard AI problems will carry more weight than a list of credentials.
• Autonomy and resourcefulness. This is a small team doing frontier work, so the role requires someone who can diagnose problems independently, make pragmatic technical decisions, and keep complex systems running. If that excites rather than worries you, this is the right position.
• Collaborative disposition. You will work daily with questions from very different disciplines: ecology, political science, geopolitics. The ability to communicate technical constraints and possibilities to non‑technical collaborators is essential. Interpersonal skills will form an important part of the assessment.

Location:

The position is based either at Grimsö or at Uppsala, with a possibility to be partly based at another academic Swedish institution with a core expertise in AI and machine learning.

Form of employment:

Fixed‑term employment 12 months with the possibility of prolongation.

Scope:

100%.

As agreed, as soon as possible after recruitment.

Application:

Please submit your application before deadline 14 July 2026. You can submit your application by clicking the button below. This is a shortened advertisement. Please refer to the full advertisement on the SLU website for complete information about the position and details on what to include with your application.