Doctoral student in Geographic Information Science (GIS) at MGeo Full-time NEW

Doctoral Student In Geographic Information Science (GIS) At Mgeo

Lund University was founded in 1666 and is repeatedly ranked among the world's top universities. The University has around 46 000 students and 8 500 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition.

Research and education at the Department of Earth and Environmental Sciences (MGeo) addresses fundamental and applied questions on the Earth's past, present and future climate and environment, including people's interactions with the natural world and consequences for human wellbeing. MGeo advances and deploys cutting-edge methods, models and technologies in environmental science, quaternary sciences, bedrock geology, paleontology, physical geography, biodiversity and ecosystem science, remote sensing, Geographic Information Science (GIS), and computational science for health and environment, to study processes spanning from the microscopic to the planetary, across all time scales.

The PhD student will be based at the GIS Centre, Department of Earth and Environmental Sciences, Lund University. The GIS Centre conducts research, education and development in Geographical Information Science (GIScience), with particular expertise in geospatial analysis, GeoAI, digital twins, and spatial data infrastructures.

The position will be part of the Centre's research activities in GeoAI and Spatial Intelligence, with a particular focus on Autonomous GIS. This emerging research direction explores how artificial intelligence, large language models and multi-agent systems can enable GIS to understand complex spatial problems, reason about geographic information, and autonomously plan and execute geospatial workflows.

The GIS Centre provides an interdisciplinary research environment where expertise in GIScience, geospatial technologies and AI meet applications in environmental analysis, urban planning, sustainability and other societal challenges. The PhD student will work closely with researchers at the Centre and have opportunities to collaborate with national and international research partners.

The Centre offers a collegial, supportive and collaborative working environment that encourages scientific curiosity, independent thinking and professional development.

Being a doctoral student

As a doctoral student, you are both admitted as a student and employed at Lund University.

As a doctoral student, you will be trained in a scientific approach. In short, you will be trained to think critically and analytically, to solve problems independently using the right methods, and to develop an awareness of research ethics. In addition, you will have the opportunity to work on projects, to develop your leadership and pedagogical skills. Throughout your studies, you will be guided by supervisors. Doctoral studies end with a thesis and a doctoral degree.

Subject and project description

The doctoral student will be admitted to third-cycle studies in Geobiosphere Science, with a specialisation in Geographical Information Science (GIScience). GIScience is an interdisciplinary research field concerned with the theories, methods and technologies for collecting, managing, analysing, modelling and communicating geographical information. The subject encompasses both fundamental methodological research and applications across environmental, societal and technological domains.

The doctoral research will focus on Autonomous GIS and Spatial Intelligence, an emerging research direction at the intersection of GIScience and artificial intelligence. Recent advances in large language models (LLMs), GeoAI and multi-agent systems are creating new opportunities to transform GIS from systems that primarily execute user-defined operations into intelligent systems capable of interpreting spatial problems, reasoning about geographical information, planning and executing geospatial workflows, and evaluating their results.

Despite these advances, important scientific challenges remain. These include the ability of AI systems to understand spatial concepts and relationships, integrate geographic knowledge with computational reasoning, select and coordinate appropriate geospatial methods, and ensure the reliability, transparency and geographical validity of autonomous analyses.

The doctoral student will investigate methods and frameworks for advancing Spatial Intelligence and Autonomous GIS, potentially addressing topics such as geospatial reasoning with LLMs, multi-agent architectures, autonomous geospatial workflow planning and execution, integration of geographic knowledge and analytical tools, and evaluation of the reliability and quality of AI-generated geospatial analyses.

The research is expected to contribute new scientific knowledge and methods in GIScience while advancing the foundations of intelligent and increasingly autonomous geospatial systems. The specific research questions and methodological approaches will be further developed in collaboration with the supervisors during the doctoral studies.

Work duties

Your main responsibility will be to pursue doctoral studies and conduct independent research in Autonomous GIS and Spatial Intelligence, leading to a doctoral thesis. You will develop research questions, investigate and develop new methods, design and conduct experiments, evaluate results, and communicate your findings primarily through publications in high-quality, peer-reviewed scientific journals.

You will work closely with your supervisors and other researchers at the GIS Centre, while gradually developing your independence as a researcher. Your doctoral education will also include relevant doctoral courses, participation in research seminars, and opportunities to present and discuss your research at national and international scientific conferences. You will be encouraged to engage in research collaborations and contribute to the Centre's academic activities and research environment. International travel may be required for conferences, research visits and collaborative activities.

In addition to your doctoral studies, you may participate in teaching and other departmental duties, corresponding to a maximum of 20% of your working time. These duties may include assisting with teaching, supervising student exercises and projects, and contributing to educational activities, particularly in GIS, geospatial analysis and GeoAI. The extent and nature of these duties will be determined based on departmental needs and your qualifications and interests.

Admission requirements

A person meets the general admission requirements for third-cycle courses and study programmes if the applicant:

• has been awarded a second-cycle qualification, or
• has satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the second cycle, or
• has acquired substantially equivalent knowledge in some other way in Sweden or abroad.

Specific admission requirements

• To meet the specific admission requirements for third-cycle studies in Geobiosphere Science with a specialisation in Geographical Information Science, the applicant must have completed an independent degree project in a relevant subject comprising at least 30 higher education credits, or have acquired equivalent qualifications. Qualifications from other relevant disciplines may also be considered, particularly when appropriate for the interdisciplinary nature of the doctoral research.

Additional requirements

• A Master's degree in Geographical Information Science, Geoinformatics, Computer Science, Artificial Intelligence, or a closely related field.
• Good knowledge of artificial intelligence and machine learning, including an understanding of fundamental methods and principles, and familiarity with modern deep learning approaches and large language models (LLMs).
• Experience with AI and machine learning frameworks such as PyTorch, TensorFlow, or similar technologies.
• Strong programming skills in Python, with the ability to develop, implement, test and evaluate computational methods and algorithms.
• Good analytical and problem-solving skills, with the ability to approach complex scientific and technical challenges systematically.
• An interest in GIScience, geospatial analysis and spatial intelligence, with the motivation and ability to develop interdisciplinary expertise at the intersection of geographical information science and artificial intelligence.
• Good ability to work independently and collaboratively, including the capacity to formulate research problems, take initiative and contribute constructively to an interdisciplinary research environment.
• Good communication skills in English, both written and spoken.

The following qualifications will be considered advantageous:

• Practical experience developing applications using LLMs, agentic AI, retrieval-augmented generation (RAG), tool integration or multi-agent systems.
• Experience with geospatial programming libraries and tools such as GeoPandas, Shapely, Rasterio, PyQGIS or ArcPy.
• Knowledge of spatial reasoning and knowledge representation, including geographic semantics, ontologies, knowledge graphs or neuro-symbolic AI.
• Experience in developing and evaluating computational methods and AI-based systems, including experimental design, software testing, version control, and integration of computational tools and APIs.
• Demonstrated scientific writing skills, particularly experience preparing manuscripts or publications for peer-reviewed scientific journals.
• Previous involvement in scientific research or publications in artificial intelligence, GIScience, GeoAI or related fields.

Selection criteria

The selection of eligible applicants will be made based on the above-mentioned qualifications and competences. In addition, personal qualifications will also be considered in the evaluation process including:

• Strong scientific curiosity and creativity, with the ability to formulate original ideas and explore innovative approaches.
• Ability to think critically, evaluate methods and results, and approach scientific challenges constructively.
• A collegial and cooperative attitude, with a willingness to share knowledge, support colleagues and contribute positively to the research environment.
• Good interpersonal and communication skills, including openness to feedback and respect for different perspectives.
• Reliability, responsibility and professionalism, including the ability to honour commitments and contribute constructively to shared activities.

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