Job Information
Organisation/Company: Vilnius University
Department: Research and Innovation Department
Research Field: Computer science » Informatics
Researcher Profile: Recognised Researcher (R2)
Positions: Postdoc Positions
Application Deadline: 7 Sep 2026 - 23:59 (Europe/Vilnius)
Country: Lithuania
Type of Contract: Temporary
Job Status: Full-time
Hours Per Week: 40
Offer Starting Date: 2 Nov 2026
Is the job funded through the EU Research Framework Programme?: Not funded by a EU programme
Is the Job related to staff position within a Research Infrastructure?: Yes
Offer Description
- Research Topic: research on methods, technologies, and practices for uncertainty-aware foundation and vision-language models for Earth observation, multi-task environmental monitoring and trustworthy geospatial AI.
- Planned Fellowship Supervisor: Assoc. Prof. Dr. Valentas Gružauskas
The candidate will be a part of a multidisciplinary and collaborative team that can engage with international partners.
- Monthly Salary: €3,634.00 gross (before tax).
- Duration of Work Contract: 12 months.
- Please note: candidates who are unable to work in Lithuania, at the premises of the institutes of the Faculty of Mathematics and Informatics at Vilnius University, will not be considered.
Prospective candidates interested in collaborating with Assoc. Prof. Valentas Gružauskas and Assoc. Prof. Linas Petkevičius in their research group will be hosted at the AI Methods Lab of the Institute of Computer Science, Faculty of Mathematics and Informatics, and can expect to work on remote sensing focused on using new fundamental vision and language models. Research on multi-task prediction models for satellite imagery covers both fundamental research and addressing practical applications.
Assoc. Prof. Dr. Valentas Gružauskas works at the intersection of AI and computational modelling, with a focus on agent-based modelling, socio-economic simulation, and modern AI methods (incl. LLM-driven agentic workflows) for decision support in complex systems. He has extensive teaching and supervision experience in AI-related topics and supervises technical projects involving LLM agents, evaluation and controllability. His research emphasises reproducible computational experiments, sensitivity/uncertainty analysis, and methodological rigour when linking empirical evidence to simulation. The postdoctoral fellow will be guided in a highly technical manner while jointly developing domain understanding with environmental monitoring and Earth observation partners.
Profiles: Linas Petkevičius Institute profile
Assoc. Prof. Linas Petkevičius has a master's degree in statistics and PhD in computer science (2020, Vilnius University) with competencies in both informatics and statistics. After his doctorate studies, Dr. Petkevičius works with machine learning. He is the co-author of 13 articles published in scientific journals with a citation index (IF) in the Web of Science (WoS) database. Previously he was an expert at the Agency for Science and the Austrian Science Foundation (FWF), a board member of the European AI Forum and a member of the editorial board of the journal Nonlinear Analysis: Modeling and Control (Q1). From 2026 he is the Head of Institute of Computer Science at the Faculty of Mathematics and Informatics. Dr. Petkevičius is teaching the Deep learning and introduction to Quantum computing courses. Currently, he is the team lead of national research project: “New generation multi-task recognition from satellite image algorithms for climate monitoring” (NEUTRINO).
Key Responsibilities & Research Lines:
The postdoctoral fellow will drive research on uncertainty-aware foundation and vision-language models for Earth observation, with the goal of making large-scale environmental monitoring systems not only accurate, but demonstrably reliable. Over the 12 months of the fellowship, core responsibilities and research directions include:
- Designing, implementing and rigorously evaluating uncertainty-aware architectures for Earth observation foundation models — including deep ensembles, Bayesian and evidential approaches, conformal prediction and post-hoc calibration — so that model outputs are accompanied by reliable, well-calibrated confidence estimates rather than point predictions alone.
- Adapting and benchmarking recent Earth observation foundation models and vision-language models (VLMs) for multi-task downstream use, covering land-cover mapping, semantic segmentation, classification, change detection and climate-related analyses, with particular attention to how spatial, temporal and contextual information is integrated within unified multimodal learning frameworks.
- Working with heterogeneous remote sensing and in-situ data: collection and harmonisation of public satellite archives (Sentinel, Landsat, MODIS and others), fusion with environmental variables, geospatial metadata and user-provided datasets, and with ground-truth measurements such as peat soil moisture, water chlorophyll concentration, solar panel performance monitoring, and other environmental sensor/IoT streams.
- Characterising robustness, transferability and uncertainty calibration across tasks, sensors and regions — quantifying how heterogeneous acquisition conditions, atmospheric disturbances, seasonal variability, incomplete observations and domain shift degrade predictive quality, and identifying the regimes in which model outputs should not be trusted.
- Implementing the resulting methods within reproducible, PyTorch-based experimentation pipelines on HPC infrastructure, supporting hyperparameter optimisation, scalable benchmarking and open-source release, and enabling future integration with emerging AI-assisted research workflows.
- Contributing to high-impact scientific publications, presentations at international conferences, and the preparation of competitive research proposals, while actively participating in the collaborative, international environment of the AI Methods Lab.
- Mentoring and knowledge transfer activities, including support for student projects and lab seminars in trustworthy AI for Earth observation and uncertainty quantification.
Where to apply
E-mail: mokslo.prodekanas@mif.vu.lt
Requirements
Research Field: Computer science » Informatics
Education Level: PhD or equivalent
Skills/Qualifications
- Research Expertise: strong background in deep learning, computer vision or remote sensing, ideally including semantic segmentation or multi-task learning.
- Experience with Vision-Language Models (VLMs) and multi-modal AI systems.
- Programming Skills in Python, PyTorch, and modern ML frameworks.
- Experience working with satellite imagery or geospatial data.
- Language Skills: good working knowledge of English (B2 level or higher), with strong written and oral communication skills.
- Familiarity with uncertainty quantification for deep learning — for example deep ensembles, Bayesian approximations, conformal prediction or calibration methods.
- Publications in machine learning, AI, computer vision, or related areas.
- Ability to design and implement AI research prototypes.
- Strong analytical thinking and problem-solving skills.
- Interest in interdisciplinary research combining AI and Earth observation.
- Good communication skills and ability to work in international research teams.
- Motivation to contribute to cutting-edge AI research and scientific innovation.
Desirable Assets (Considered as Advantages):
- Experience in academic mentoring, including leading practical seminars or co-supervising student theses within an international university environment.
- Demonstrated experience or motivation in drafting technical grant proposals for international funding agencies.
Specific Requirements
Specific Institutional Preference: Strong preference will be given to candidates who obtained their PhD degree from prestigious international research institutions outside of Lithuania that possess an established, world-class research framework.
Internal Application form(s) needed
APPLICATION FOR VU POSTDOCTORAL FELLOWSHIPS (FORM)_0.pdf
English
(61.12 KB - PDF)
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Additional Information
Benefits
A great opportunity to conduct research in a friendly team, with good working conditions and the chance to gain new professional experience.
Eligibility criteria
Applicants must satisfy the eligibility requirements for postdoctoral fellowships at Vilnius University:
- The candidate must hold a doctoral degree awarded by a foreign institution (preferred) or a Lithuanian science institution other than Vilnius University.
- No more than 5 years must have elapsed since the awarding of the PhD degree. This 5-year window can be formally extended for documented periods of maternity, paternity, or parental leave.
Selection process
The evaluation follows a standard, transparent merit-based academic review process. Candidates will be ranked based on the alignment of their competencies with the research description and the quality of their scientific publication record.
Please note: To optimize the administrative process, only shortlisted candidates who pass the initial screening phase will be contacted and invited for an online interview. We kindly thank all applicants for their time and interest in this position.
Additional comments
Required Documents:
Applicants must submit a single application package via email, which consists of the official application form and its mandatory attachments:
- Official Application Form: the completed and signed Application Form for Vilnius University Postdoctoral Fellowships (ensuring the fields “Planned fellowship supervisor” and “Title of the fellowship” match the details of the Offer).
- Mandatory Form Attachments (as specified in the application template):
- Curriculum Vitae (CV): A free-form description of your life and scientific activities.
- List of Publications: A structured list highlighting up to 10 of your most important scientific publications and/or patents.
- Digital Copy of PhD Diploma: Required by the university administration for the formal 5-year eligibility window verification check.
Application Submission:
Applications must be submitted electronically to the Vice-Dean for Science of the Faculty, via email at: mokslo.prodekanas@mif.vu.lt.
Important: Please ensure the email subject line states exactly “postdoctoral fellowship” to guarantee proper routing, indexing, and official registration.
Work Location(s)
Number of offers available: 1
Company/Institute: Institute of Computer Science, Faculty of Mathematics and Informatics of Vilnius University
Country: Lithuania
State/Province: Vilnius District
City: Vilnius
Postal Code: LT-03225
Street: Naugarduko str. 24
Contact
State/Province: Vilnius District
City: Vilnius
Website: https://www.vu.lt/en/
Street: 3 Universiteto street
Postal Code: LT-01513
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