31 phd-learning-"https:"-"Ikerbasque---Basque-Foundation-for-Science" positions in Finland
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conducting research at Hanken to apply for Marie Skłodowska-Curie Postdoctoral Fellowships. Marie Skłodowska-Curie Postdoctoral Fellowships (MSCA PF) target researchers holding a PhD who wish to acquire new
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. Participate in project implementation and reporting activities. Contribute to academic life by supervising PhD, MSc and Bachelor students, and assisting in teaching activities. Engage in training opportunities
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funding. The start date is 1 September 2026. The appointee is expected to reside in the Helsinki Metropolitan Area during the employment. QUALIFICATIONS AND EVALUATION Applicants are required to hold a PhD
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about micronutrient content, intake, digestibility and bioavailability from plant-based diets. Part of the PhD study is planned as external stay at Valio (Helsinki, Finland) for one month to learning
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micronutrient content, intake, digestibility and bioavailability from plant-based diets. Part of the PhD study is planned as external stay at Valio (Helsinki, Finland) for one month to learning legume
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integrate large population-level cohort analyses with cutting-edge genome editing and single-cell approaches in cell lines and in patient samples. The research group is led by Mikko Myllymäki, MD, PhD and
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the PhD study is planned as external stay at Valio (Helsinki, Finland) for one month to learning legume fermentation in an industrial scale, investigate the effect of bioprocessing and gain insight to R&I
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diets. Part of the PhD study is planned as external stay at Valio (Helsinki, Finland) for one month to learning legume fermentation in an industrial scale, investigate the effect of bioprocessing and gain
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Doctoral Researchers (PhD students) to work on deep learning methodologies for machine and robot perception. These positions are funded by the Horizon Europe project OPERA (Open Perception, Learning, and
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inequalities and Sobolev-type spaces (with Hytönen and/or Korte), 3. Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic