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Luleå University of Technology experiences rapid growth with world-leading expertise within several research areas. Our scientific and artistic research and education are conducted in close
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models
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dynamics for shape change. A further aspect of the project is learning and calibrating these models from data using data-driven inference methods. Who we are looking for Required qualifications A doctoral
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on the development and application of advanced modelling and machine learning methods, and may involve the following areas: Dimensionality reduction. Data-driven methods for estimating dynamical models Data-driven
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learning, multivariate modeling, or data-driven approaches, as well as interest or experience in the integration of neuroimaging with genetic or transcriptomic data, is considered a strong merit. Prior
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-induced variability, residual stresses, surface integrity, inspection and qualification requirements, and sustainability. Capturing these effects in whole-engine, system-level models remains an open
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, noise modeling, signal processing, or data-driven methods Documented research experience through scientific publications Proficiency in English, both written and spoken As a person, you are curious
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-based methods are particularly well-suited to bridging the molecular and experimental scales and will be central to this effort. The research will be performed in the Theory and Modelling group (https
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Compular AB, where the postdoctoral researcher will engage with industrial modelling workflows and application-driven challenges. The main duties involved in a post-doctoral position is to conduct research
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AI-driven analyses, and patient-oriented research, WHOLE aims to generate new knowledge, diagnostic innovations, and more equitable models of care in close collaboration with patients and healthcare