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Description Who We Are: The Cutsail research group at Ludwig-Maximilians-Universität (LMU) Munich is seeking a highly motivated PhD student / research assistant for full-time, on-site work in the
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Europe | 19 days ago
, PortugalSupervisors: Dr. J. Pedro (INF), Prof. L. Cancela (Iscte)Apply here: https://match.iscte-iul.pt/phd-candidates-profiles/apply-to-dc-positions/Job information:Coordinator: Iscte – Instituto Universitário de
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Programme description:The ICN2 offers a competitive PhD Programme to talented students who wish to develop a research career at one of the top European institutions in the field of nanoscience and
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the supervision of Dr. Robert Froese. This PhD student will address the DIVERSE project theme focused on the assessment and mapping of functional diversity and redundancy at the national scale, using remote sensing
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smart technologies in the bioeconomy, agricultural, food and environmental policy impact analysis, and/or production efficiency and food markets. leading to an internationally competitive PhD degree and
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PhD/postdoc fellows) to carry out experimental projects with an EMBL host research group, thereby allowing them to build new networks, experience the international and interactive environment at EMBL
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are highly desirable. Minimum Requirements • A master's degree in a relevant field, such as architecture, landscape architecture or historic preservation. A professional degree or PhD is preferred. In
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deep learning to solve complex, high-impact problems. The ideal candidate will have a strong grasp of diverse machine learning techniques and a passion for experimenting with model architectures, feature
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technologists to push strategies into production What we're looking for PhDs graduating by Summer 2026 or postdocs in quantitative fields such as Mathematics, Physics, Statistics, Electrical Engineering, Computer
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-impact problems. The ideal candidate will have a strong grasp of diverse machine learning techniques and a passion for experimenting with model architectures, feature engineering, and hyperparameter tuning