Job Title:  Junior Visiting Researcher

We are seeking a Junior Visiting Researcher for a global research project on “An Adaptive Intelligence Framework for Nested Energy System Transitions” funded by Schmidt Sciences. We seek a candidate ideally with a strong background in machine learning, artificial intelligence, data science, and cloud-native infrastructure to support the development of advanced energy demand models at the intersection of building energy modeling, urban energy systems and climate change mitigation. You will train, fine-tune, and improve AI/ML models, working with real-world building energy models and geospatial datasets at global scales. This is a hands-on research role that combines ML methodology with practical infrastructure skills, designing scalable data pipelines, working with containerized environments, low-energy architecture, and maintaining reproducible computational workflows on cloud platforms. You will contribute to peer-reviewed publications, new ideas for how the models can be used for advancing sustainable urban solutions, and help translate cutting-edge methods into tools that inform climate policy and energy transition pathways. The role is ideal for someone excited by research-focused work and advancing sustainability, comfortable with messy data and cloud-native development practices and interested in climate/energy applications of AI.

Project Context

This position is embedded in the NEST project (funded by Schmidt Sciences), a research initiative to develop adaptive AI methods for integrating city, regional, and national energy transition models. Our team at CEU focuses on building-scale energy demand modeling and supply-demand integration, anchored in geospatial analysis and machine learning. You will be working within an international consortium including IIASA, Imperial College London, the Active Inference Institute, Open Earth, and others.

Duties & Responsibilities 

• Design and implement cloud native data pipelines and scalable infrastructure for ingesting, processing, and serving large geospatial, building energy and climate datasets
• Train, fine-tune, and evaluate machine learning models for building energy demand projection, renewable energy supply forecasting, and urban energy system optimization
• Work with large geospatial, building energy and raster datasets (climate grids, building footprints, urban morphology) using Python geospatial libraries and climate data (MERRA-2, ERA5, CMIP)
• Develop clean, well-documented Python code with Git/GitHub and set up reproducible workflows (environments, testing, documentation)
• Contribute to documentation, project reporting and peer-reviewed publications
• Design and execute experiments to evaluate model performance, uncertainty, sensitivity, and transferability across geographies and scales
• Engage in code review and pilot project work across institutions

Qualifications

Required

• Strong foundation in machine learning: understanding of supervised/unsupervised methods, model evaluation, feature engineering, and common pitfalls
• Artificial Intelligence related experience
• Solid proficiency in Python for data science and ML
• Experience training and fine-tuning ML models (in academic or professional projects)
• Experience with cloud platforms (Azure, AWS, Google Cloud) and basic data engineering (ETL pipelines, databases)
• Experience with containerization (Docker) or workflow orchestration
• Experience with heterogeneous real-world datasets, ability to assess data quality, handle missing values, and work iteratively with imperfect information
• Strong written and spoken communication skills in English
• Masters degree (or equivalent) in a quantitative field (data science, computer science, environmental science, physics, engineering, or related) · Demonstrated ability to work independently and manage tasks with self-direction; willingness to learn new tools and methodologies

Nice to have

• Proficiency with Git/GitHub for version control and collaborative development
• Modeling experience
• Familiarity with climate and earth system data (NetCDF, GeoTIFF)
• Experience working with geospatial data and/or geospatial modelling (e.g., GeoPandas, rasterio, xarray, or similar tools)
• Understanding of active inference approaches
• Prior contribution to peer-reviewed publications or research projects
• Background in architecture, building engineering, climate science, energy systems, or environmental engineering Skills and Competencies
• Analytical and experimental mindset: ability to design and test hypotheses, interpret results critically, and iterate on methods
• Problem-solving orientation: comfort tackling ambiguous research questions with incomplete information
• Collaborative mindset: strong communication, openness to feedback, and ability to work across institutions and disciplines
• Self-directed learning: willingness to master new tools, read literature, and adapt to evolving project needs
• Research-oriented: interest in contributing to scientific understanding, solving environmental problems, not just implementing existing methods

What CEU offers

• Opportunity to work on cutting-edge research at the intersection of AI/ML, architecture, energy systems, and climate solutions
• Exposure to real-world geospatial data, energy datasets, and global-scale modeling challenges
• Work in a world-class international research consortium with global institutional leaders in the topic
• Dynamic, international academic environment at CEU with strong institutional support
• Opportunity to contribute to peer-reviewed publications
• Hands-on experience with state-of-the-art tools and methods in ML and geospatial science
• Flexible working arrangements and collaborative, supportive research team culture

How to Apply

Please submit a letter of motivation, detailing your relevant qualifications, skills, experience, as well as how this position fits into your career goals. Please attach a detailed CV, and a sample first-authored publication, if available. Identify two references who can be contacted to comment on your professional skills.

Optional: may submit other relevant documentation on specific relevant skills.

CEU is an equal opportunity employer and values geographical and gender diversity, thus encouraging applications from women and/or other underrepresented groups. Since CEU strives to increase the share of women in professorial positions, given equal qualifications, preference will be given to female applicants. CEU recognizes that personal and family circumstances shape the trajectory of one’s career and working patterns. As such, and in line with CEU’s promotion of Equal Opportunities, we encourage applicants to detail periods of leave, part-time work or other such situations in their applications so that the Search Committee is able to assess an applicant’s academic record fairly in the context of their circumstances. Any declaration of personal and family circumstances is voluntary and will be handled confidentially and only considered in so far as it impacts on the academic career of an applicant.

About CEU

One of the world’s most international universities, a unique founding mission positions Central European University as both an acclaimed center for the study of economic, historical, social and political challenges, and a source of support for building open and democratic societies that respect human rights and human dignity. CEU is accredited in the United States and Austria, and offers English-language bachelor's, master's and doctoral programs in the social sciences, the humanities, law, environmental sciences, management and public policy. CEU enrolls more than 1,400 students from over 100 countries, with faculty from over 50 countries.

In 2019, CEU relocated from Hungary to Austria as the Hungarian government revoked its ability to issue US-accredited degrees in the country. As a result, CEU offers all of its degree programs in Vienna, Austria; and retains a non-degree, research and civic engagement presence in Budapest, Hungary, through its CEU Democracy Institute, the Institute for Advanced Study, the CEU Summer University and The Vera and Donald Blinken Open Society Archives (OSA), and its Hungarian language public educational programs and public lectures.

For more information, please visit https://www.ceu.edu/.