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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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. 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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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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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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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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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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your materials in English in one PDF file. The file must include: Application (cover letter) CV Academic Diplomas (MSc/PhD – in English) List of publications Research ideas on the scientific topic
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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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and other skills or experiences relevant for this position. Copies of relevant diplomas (MSc/MA and PhD). Scientific qualifications. A complete list of publications should be attached with an indication