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engineering Architecture Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Estonia Application Deadline 31 Jan 2026 - 23:59 (Europe/Tallinn) Type of Contract Temporary Job Status
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Reponsibilities Perform analysis of large-scale quantitative and qualitative datasets as part of research projects. Use of new tools (machine learning, LLMs, OCR) to collect and process data. Work with bank and
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computational analyses of single-cell, spatial transcriptomics, and multi-omics datasets Developing and maintaining reproducible, well-documented analysis pipelines Applying and adapting machine learning and AI
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of cybersecurity. Examples areas of interest include, but aren’t limited to: ● Problems at the intersection of cybersecurity and artificial intelligence/machine learning (AI/ML) ● Systems and
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) Neuromodulation approaches (TMS, tDCS, TUS) Neurogenetics Computational modelling (machine learning, reinforcement learning) Our research bridges scales (local circuits to global networks) and species (humans, mice
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computing modules within the School of Computing and Engineering commensurate with their technical knowledge and prior experience. The duties and responsibilities associated with the role are: To teach on and
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Python Arduino and C++ (Physical Computing) Creation of interactive objects and components Machine Learning and Natural Language Processing Specific Requirements Candidates must hold a PhD in engineering
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by colloids, as well as methods for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion
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self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed to irrigate
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, supply chain systems, and transportation systems. We also currently host methodological research in data analytics, machine learning, human systems engineering, optimization, simulation, and stochastic