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comprehensive support for large‑scale multi‑omic data generation/analysis and transformation/embryogenesis services for functional validation. Nathaniel is also an associate group leader at the Science for Life
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machine-learning tools. Data analyzed include precursors such as volatile organic compounds, aerosol number and mass concentrations, chemistry, biological particles, cloud and ice condensation nuclei, light
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within the Data Science & AI division (DSAI). With 30+ nationalities and strong industry/academic ties, we offer a dynamic, collaborative ecosystem. The AI and Machine Learning in the Natural Sciences
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Laszlo Bakó (molecular biology) and Torgeir R. Hvidsten (comparative genomics). The environment provides comprehensive support for large‑scale multi‑omic data generation/analysis and transformation
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-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 for complex data, including temporal data
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) Chalmers/University of Gothenburg drives cutting-edge research in machine learning and its application within the Data Science & AI division (DSAI). With 30+ nationalities and strong industry/academic ties
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17 Jan 2026 Job Information Organisation/Company Lunds universitet Department Lund University, LTH, Department of Energy Sciences Research Field Engineering Researcher Profile Recognised Researcher
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and free-energy calculations in explicit solvent. The postdoctoral researcher will employ machine-learning-accelerated methods throughout the workflow, contribute to the development of new computational
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of energy. Approximately 25 colleagues work in the division, including 15 PhD students. On the international level we collaborate with universities and institutes in Europe, Asia, and North America