Laimonas Kairiukstis
The candidate should be a Master’s student working in Computer Networks and Communication Protocols, with a strong interest in the Internet of Things (IoT) and LoraWan. Preferred qualifications include: • Solid understanding of electronics systems, network architectures and communication protocols. • Familiarity with IoT platforms, embedded systems, LoraWan or wireless sensor networks. Skilled in Data Preprocessing • Good programming skills (e.g., Python, C/C++, or similar). • Motivation to work on research-oriented projects involving IoT applications, network connectivity, and data management. • Ability to work independently and collaboratively in an international environment.
KAUNO KOLEGIJA
IoT Calibration as a service (IoT-CaaS): a study in the context of data aggregation using LoraWan infrastructure This internship offers a unique interdisciplinary experience that bridges two major fields: IoT/Networking and Data Science/Machine Learning.
Leveraging Technical Depth: The intern will directly apply his strong background in Networking (LoRa modules) and ML/AI (TensorFlow, PyTorch, Data Preprocessing) to a complex, real-world research problem. Access to a dedicated IoT testbed (LoRaWAN network) and high-quality reference sensor data. Expected beneficits: Academic Advancement: A high-impact publication suitable for a scientific conference or journal based on the CaaS ML model. Career Enhancement: Gain specialized expertise in the critical and highly marketable area of IoT metrology and predictive maintenance. Interdisciplinary Output: The development of the predictive CaaS model will constitute a novel deliverable, improving the accuracy and the sustainability of LoRaWAN-based IoT systems. Beneficits: Research Continuity: Establishes a concrete basis for potential future doctoral collaboration (DC) by validating key technical hypotheses and methodologies. Internationalization: Strengthens research collaboration within UNINOVIS Alliance in the fields of IoT and AI. Infrastructure/Resources Available: • Access to a fully operational LoRaWAN research network (gateway and nodes) installed at KK HEI • Dedicated indoor air quality lab equipped with high-precision reference sensors for ground-truth data collection. • Computational resources, including access to GPU-enabled servers for training and testing ML models (TensorFlow/PyTorch environments). • Direct supervision and mentorship from experts in calibration and metrological methods
3 months
KK HEI is currently conducting a bilateral PHC cooperation project with USPN entitled “Monitoring metrological data using low-cost IoT measuring devices in indoor environments,” funded by Campus France and the Lithuanian Academy of Sciences. The proposed internship project is based on the objectives of WP3 of this bilateral project, allowing the proposed internship activities to be based on concrete and immediate scientific activities, ensuring the continuity and synergy of the ongoing PHC project. The internship will thus accelerate results and joint publications in the field of IoT metrology.