18 big-data-machine-learning-phd Postdoctoral research jobs at UNIVERSITY OF HELSINKI
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have solid skills in programming and working with libraries for training and using machine learning models. Previous experience in managing large volumes of data and high-performance computing is
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volumes of audiovisual data is essential. The appointee must have solid skills in programming and working with libraries for training and using machine learning models. Previous experience in managing large
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16 Apr 2026 Job Information Organisation/Company UNIVERSITY OF HELSINKI Research Field Communication sciences Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Application
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duration of 1/8/2026-31/7/2028 or as agreed. The position is part of Helsinki GSE activities on Data Room related research. The Data Room is an independent unit at VATT Institute for Economic Research, with
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collaborate effectively in large inter-disciplinary teams from different cultural backgrounds Strong ability to support Masters-level teaching in the human dimensions of natural resource management Strong
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of genomic data will be generated from historical museum specimens, which typically yield highly fragmented DNA. The postdoctoral researcher will be primarily responsible for: Designing and analysing genomic
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19 Mar 2026 Job Information Organisation/Company UNIVERSITY OF HELSINKI Research Field Agricultural sciences Economics Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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completed a PhD in economics or agricultural economics, who have solid training in econometrics and applied microeconomics and experience working with administrative firm-level data. The doctoral degree must
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(linking phenotypes, imaging, cytometry, or other readouts to transcriptomics) Statistics / machine learning for biological inference (model validation, differential state testing, embeddings/classifiers
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-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning