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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental
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under construction at SURF. The expected start date for this position is as early as August 1, 2025. Qualifications: A PhD in high-energy physics or a related field is required. Experience with detector
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at the Faculty of Medicine, University of Helsinki. The project will focus on using and extending deep learning-based approaches developed within the group to integrate bulk multi-omics cancer data. The Kuijjer
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frameworks such as GTSAM, G2O, or similar; computer vision frameworks like OpenCV; and/or deep learning frameworks such as PyTorch and TensorFlow Prior experience with industry or publicly funded research
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capabilities. Experimental design, laboratory execution, data analysis, and statistical quality control. Required Education PhD Degree in Biochemistry, Molecular Biology, Molecular Genetics, or closely related
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. Are you interested in applying your machine learning and deep-learning expertise to develop cutting-edge ecological and environmental research? The Senckenberg Gesellschaft für Naturforschung invites you to
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with deep learning libraries (e.g., PyTorch) Ability to organise and prioritise work to meet deadlines with minimal supervision Strong written and verbal communication skills, with the ability to convey
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/C++; hands-on experience with deep learning libraries (e.g., PyTorch) 5. Ability to organise and prioritise work to meet deadlines with minimal supervision 6. Strong written and verbal
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knowledge about recent neural network architectures for machine learning (e.g., CNNs, RNNs, GANs) have considerable experience with a deep learning framework are curious about the cross-field between signal
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interpretable deep neural networks is required. Candidate must have published in top journal and conference at least one scientific paper in interpretable machine learning (not explanations of black boxes) among