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Description Main supervisor: Ants Kallaste Co-supervisor: Anton Rassõlkin The research Within this thesis, the PhD candidate will learn about the control and application of additively manufactured special types
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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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data collection strategy using the Internet of Things, sensors and NMEA 2000, a data analyzing plan using established guidelines and machine learning tools. We offer: A fully funded PhD position for four
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or willingness to learn Estonian language. We offer: The opportunity to work on an interdisciplinary and internationally attractive research topic. Joint Supervision and Expertise: The PhD position is offered in
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within a Research Infrastructure? No Offer Description We are looking for a highly motivated and ambitious PhD candidate with experience in either biomedical engineering, machine learning, polymer
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. Advanced modeling techniques, such as surrogate modeling, machine learning, and physics-informed neural networks, will be applied to accelerate simulations and enable real-time performance. A strong emphasis
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This PhD project will explore a
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and development activities in the fields of atomistic simulations, including density functional theory, machine learning, and molecular dynamics. The work involves theoretical and experimental research
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. To this end, the candidate is expected to have a good knowledge of programming tools and acquire knowledge about our custom systems during the initial stage of the doctoral studies. Responsibilities and
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, and semi-structured interviews. Using European competence models (e.g., GreenComp, LifeComp), the project will develop flexible lifelong learning models that universities can adapt to different