Researcher — AI & Machine Learning (Track A: GEOAI or Track B: General AI/ML)

Employer: FIT CTU in Prague (Faculty of Information Technology, Czech Technical University in Prague)

Workplace: Data Science Laboratory, Department of Applied Mathematics

Location: Prague, Czech Republic

Employment: Full-time

Start date: March 1, 2026 or later (by agreement)

On-site requirement: Work on-site at least 3 days per week (no fully remote option)

About the position

The Data Science Laboratory at FIT CTU in Prague is hiring researchers to strengthen our work in Artificial Intelligence and Machine Learning. We offer two research tracks under a single posting:

• Track A — GEOAI Researcher (2-year position)
• Funded within Horizon Europe projects PoliRuralPlus and FOCAL, focusing on geospatial data, Earth observation, and AI for real-world territorial challenges.
• Track B — Researcher in AI & Machine Learning (project-funded)
• Focused on high-quality AI/ML research and international collaboration. This role is intended primarily for researchers who do not need to teach and will be funded from research projects.

Both tracks emphasize independent research, excellent publication output, and collaboration with international partners in academia and industry.

What you will do (common to both tracks)

• Conduct original research in AI and machine learning, from problem formulation through experiments to publication.
• Publish results in strong international venues (top conferences and journals in AI/ML/data mining and related fields).
• Collaborate with international academic and industry partners; contribute to joint research and dissemination.
• Mentor students (e.g., thesis supervision, research internships) and contribute to a supportive lab culture.
• Present results internally and externally (seminars, workshops, project meetings).

Track A — GEOAI focus (2-year position)

Research scope

You will work on GEOAI topics such as:

• Geospatial machine learning, spatial statistics, and spatiotemporal modeling
• Remote sensing / Earth observation (EO) and multimodal geo-data fusion
• Learning with heterogeneous, noisy, and sparse spatial signals
• Methods and applications in GIS-aware AI (e.g., land use, mobility, socio-economic and environmental signals)

Project environment (Horizon Europe)

• Active participation in EU consortia (research coordination, workshops, dissemination).
• Contribution to project deliverables and scientific/technical reporting.
• Opportunities to co-author publications with international partners and build visibility in the GEOAI community.

Track B — General AI & Machine Learning research (project-funded)

Research scope

Depending on your profile and current projects, topics may include:

• Machine learning methodology and representation learning
• Deep learning (including modern architectures) and robust evaluation
• Data mining, learning from structured/relational data, or applied ML
• Interdisciplinary AI/ML collaborations with academic and industry partners

This role prioritizes research output and collaboration; teaching is not expected as a primary duty.

What we offer

• A research role with strong emphasis on publishing and scientific impact.
• Collaboration in international consortia and access to an active network of academic and industry partners.
• Supportive, research-driven environment in Prague with opportunities to shape your direction and grow visibility.
• Access to students and a lab setting that encourages initiative, open discussion, and high research standards.
• Competitive compensation based on experience and the specific funding source/track.

Required qualifications (both tracks)

• PhD (or near completion) in computer science, applied mathematics, geoinformatics, or a related field.
• Strong publication record in reputable international venues (conferences/journals).
• Solid background in machine learning (theory and practice) and ability to deliver results independently.
• Excellent communication skills in English.
• Ability to work independently while collaborating effectively in an international environment.

Beneficial skills / experience

Track A (GEOAI):

• Experience with geospatial/EO data, remote sensing, GIS platforms, or spatial analytics
• Programming skills: Python, ML frameworks, geospatial toolkits
• Prior participation in Horizon Europe or similar international projects

Track B (General AI/ML):

• Experience with international research projects and collaboration
• Track record in successful grant applications (nice to have)
• Interest in interdisciplinary work and applied research impact

Application materials

Please submit:

• CV (curriculum vitae)
• Cover letter (state your preferred track: A – GEOAI, B – General AI/ML, or either; briefly describe your research focus and publication highlights)
• Contact information for 2–3 referees

Please submit the application for the GEOAI Researcher position by January 30, 2026, and for the General AI position by April 20, to the email address pam@fit.cvut.cz