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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 the attributes listed in the requirements section are strongly encouraged to apply. Education Requirement: PhD or equivalent in psychology, neuroscience, cognitive science, biomedical engineering
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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
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. Qualifications • PhD degree in computational biology, bioinformatics, mathematics, physics, engineering, computer science, or a related field. • Computational background in areas such as AI/deep learning
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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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in both marketing and other relevant journals contained within the Academic Journal Guide (AJG; formerly ABS) ranking list. The research of the department covers broad areas (often cross-disciplinary
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learning for dynamical systems, system identification and control systems. This can involve co-tutoring of PhD students or early R&D cooperative studies. contributing to the development of advanced benchmark
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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it is not required that the candidate possess a deep theoretical understanding of AI, practical proficiency in using AI will be considered in the selection process. Applicants must hold a PhD prior to
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algorithms; experience in 3D/4D (X-ray tomography) image processing; experience in machine-/deep-learning based image analysis; knowledge of tomographic reconstruction methods; experience in materials research