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and the Probabilistic Machine Learning Lab (https://www.helsinki.fi/en/researchgroups/probabilistic-machine-learning ; groups of Acerbi and Klami) at the Department of Computer Science, aligning with
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, selectivity and phenotype mechanisms directly in primary human cells and employ machine learning methods for data analysis. This includes activity-based profiling, global- and phosphoproteomics and phenotype
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at the time of accepting the position (a net period of time, which does not include parental leave, military service etc.) good skills in spoken and written English prior experience and knowledge in magnetic
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The Machine Learning for Health team at the Institute for Molecular Medicine Finland (FIMM) , University of Helsinki, is currently seeking a highly-motivated postdoctoral researcher to join our team
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appreciated, including dynamic, stochastic and other quantitative approaches in conditions of uncertainty. Experience in machine learning and other AI methods is a benefit, as is familiarity with the bioeconomy
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on themes such as human–machine interaction, cognitive biases, digital transformation, and AI-mediated environments, aiming to deepen understanding of how people form knowledge and make decisions in
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AI Literacy” (https://ceur-ws.org/Vol-4138/paper3.pdf ). About the position You will support the further development of COMIG and contribute to empirical data collection, analysis, and reporting. You
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international collaborations in multi-disciplinary research ranging from the development of data analysis and machine learning methods to applications in omics research. Researchers in Finland have access
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implementation in Fire Dynamics Simulator CFD code Experimental validation of simulations Machine learning for accelerated evaluation of radiative properties The doctoral degree will be awarded upon successful
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collaborate with partners at ELLIS Institute Finland. Target publications at top-level AI, ML, and NLP venues. Who we are looking for Requirements Ph.D. in Machine Learning, Natural Language Processing