895 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" "U.S" positions in Sweden
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teaching environment at the department. The main language of the PhD program is English. However, non-Swedish speaking students are expected to acquire basic skills in Swedish during the period of employment
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metaproteomics approaches Analyzing large-scale multi-omics and clinical datasets to investigate individual metabolic responses to diet. The work includes applying advanced statistical and machine learning methods
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working at SLU by visiting: https://www.slu.se/en/about-slu/work-at-slu/ Location: Grimsö wildlife research station Form of employment: Fixed-term employment for 4 months, with a possible extension. Scope
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programs for both Swedish and international students. Read more at https://www.biology.lu.se/ . Find more reasons why Lund University and the Faculty of Science are right for you here and here , and learn
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. Please visit the following homepage for more information on the Department: https://www.slu.se/en/about-slu/organisation/departments/Animal-Biosciences/ Read more about our benefits and what it is like
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complex behavior under demanding operating conditions presents a significant modeling challenge. This project addresses that challenge by combining machine learning with constitutive modeling, while
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network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce using UPSC transformation and somatic embryogenesis pipelines; evaluating drought
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forest growth, survival and biodiversity for the future use and conservation of forests. More information: https://www.slu.se/en/wiforce . The work aims to generate new knowledge and contribute
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solutions across the natural sciences. Your workplace You will be employed at the Department of Mathematics in the Division of Applied Mathematics, https://liu.se/en/organisation/liu/mai/tima . The research
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projects in data-driven nutrition, such as: statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome