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, characterization, and optimization of novel compatible electrolyte/electrode systems for the next generation Li-, Na- and Zn-ion batteries Supervisor: Linas Vilčiauskas, Electrochemical Energy Storage Group
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the interactions between ink and fiber, optimize track architecture, and ensure long term functional stability. This research direction is highly promising due to its broad applicability in health monitoring, sports
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against dynamic loads and fire impacts Research and development of AI-optimized, energy-efficient and sustainable small-batch gasification technology Development and Investigation of Wide-Bandgap
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learning, optimization, training pipelines, evaluation metrics, and validation strategies. Experience with multimodal AI (CV, NLP, tabular, sensor or IoT data). Familiarity with MLOps, versioning tools
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. Hands-on experience with Retrieval-Augmented Generation (RAG), vector databases (FAISS, Milvus, Pinecone) and embeddings. Excellent understanding of deep learning, optimization, training pipelines