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Field
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expand current technology to include automated live analysis, integrating machine learning algorithms capable of interpreting the complex behavioural patterns of mussels in response to environmental stress
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anticipated include imaging phantom-based evaluation of quantitative SPECT/CT; reconstruction algorithm development; establishing a secondary standards laboratory for unsealed source metrology through gamma
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7th January 2026 Languages English English English The Department of Biology has a vacancy for a PhD Candidate in Plant Evolutionary Ecology: plant-parasite interactions under climate warming Apply
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4 Dec 2025 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Biology Research Field Biological sciences » Biology Researcher Profile
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the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our group, you get the opportunity to use the latest algorithms in machine learning for improving
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description The BM2 Lab is an interdisciplinary research group at the interface of physics, computer science & biology interested in understanding morphogenesis, bio-mechanics and their interaction in living
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representation. Key aims include improving the generalizability, interpretability, reasoning and causal grounding of these models, developing new optimisation algorithms with biologically meaningful regularisation
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or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 30 days ago
challenges facing society today. At the Institute of Radiopharmaceutical Cancer Research scientists (f/m/d) from the fields of physics, chemistry, biology, pharmacy, immunology, medicine and IT develop
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Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms for the identification of antibiotics-associated proteins and antimicrobial