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fairness, transparency, or trust. The PhD research takes a dual perspective. This means that it examines both the experiences and expectations of end-users of AI-enabled services and the strategic decisions
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research teams at TalTech (Dr. Alan Tkaczyk & Prof. Alar Konist) and the University of Manchester (Dr. Laurence Stamford), with extensive experience available at the UK National Nuclear Laboratory (UKNNL
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prioritize the most impactful areas where energy balance could be positively impacted by data-driven or technological solutions. Propose, experiment on, and evaluate novel approaches that would speed up
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highly motivated and ambitious PhD candidate with experience in either biomedical engineering, machine learning, polymer technology, physics, electrospinning, or similar fields,to join our Lab- on-a-chip
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. The work includes both theoretical modeling and experimental validation, addressing development of software-defined power electronics, droop control adaptation to weather conditions, integration with
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researcher and as part of an international team capacity and willingness to provide assistance in organizational tasks relevant to the project The following experience is beneficial: experience in Balanced
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robustness of Autonomy 2.0 pipelines. The successful candidate will join an interdisciplinary research team and contribute to experimental validation using TalTech’s autonomous shuttle platform (iseAuto
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. Responsibilities and tasks Compile an analytical framework for examining experimental approaches to analyze green fire-retardant pretreat- ment methods before and after wood densification. Study and test the
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spoken and written English language. Willingness to travel to international and domestic project meetings. The following experience is beneficial: Experience with Life Cycle Assessment (LCA) tools and
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/ Materials Science / Mechanical or equivalent Experience in any/all of the following fields: alloy design, powder metallurgy, additive manufacturing, thermodynamics, materials characterization, and materials