AI Training Officer

About the role

Join us to shape AI training at EMBL Join us to shape EMBL AI , a new EMBL-wide initiative that aims to exploit the full potential of AI-based approaches to advance scientific discovery. The European Molecular Biology Laboratory (EMBL) has long been a pioneer in developing and applying Artificial Intelligence (AI) to advance research in genomics, structural biology, and drug discovery. Innovative AI models like AlphaFold have already revolutionised protein structure prediction, and EMBL AI is now building the infrastructure, expertise, and community needed to take this further - across all of EMBL's missions and the wider life sciences research community. Training is central to this ambition. Your role will bridge EMBL AI and the Data Science Centre (DSC), and you will work closely together with EICAT, EMBL's International Centre for Advanced Training. As AI Training Officer, you will play a key role in ensuring that researchers at EMBL and beyond have the skills, confidence, and critical understanding to use AI effectively. Drawing on the expertise of EMBL-AI scientists and the DSC, and building on EICAT's institute-wide training infrastructure and networks, you will contribute to developing a world-class AI training portfolio - one that spans practical tools, biological applications, large language models, and respon sible AI practice. Your role You will design, develop, and deliver training programmes for two audiences: EMBL's internal research community and external academic researchers. This includes both structured group training and direct one-on-one consulting with researchers - starting from their specific scientific problems and needs, rather than from methods alone. Working at the interface of cutting-edge AI research and scientific education, you will translate complex AI methods into accessible, hands-on learning experiences - from practical machine learning and AI for biology, to foundation models, agentic frameworks, and their responsible adaptation to real scientific needs. Your responsibilities include Collaborate with EMBL scientists to identify training needs and translate current AI research into accessible course content Design, create and deliver high-quality training workshops, courses, and online materials across four core areas: practical ML and deep learning, AI for life sciences and bioinformatics, LLMs, generative AI and other foundation models, and scientifically sound and ethical AI - building on and contributing to existing open-source content rather than duplicating it Develop reusable open learning resources - including Jupyter/quarto notebooks, tutorials, datasets, and instructional guides - in line with FAIR and open science principles, with an emphasis on building shared, community-owned material rather than locally maintained variants of widely available content Run in-person and virtual training events at EMBL Heidelberg and, where relevant, across EMBL's network of six European sites Conduct training needs analysis and gather learner feedback to continuously improve training quality and long-term impact Represent EMBL-AI in external training communities, including The Carpentries, ELIXIR Training, and de.NBI, contributing to shared standards and resources Support grant applications and reporting related to training activities Maintain training communications and web content, and support the dissemination of EMBL-AI training activities The role is based at EMBL Heidelberg but will involve interactions with other EMBL sites and some international travel. You have An advanced university degree in a pedagogical, computational, quantitative, or life science discipline with a track record in computational work Experience in collaborative design, creation and delivery of technical curricula and training or teaching, including hands-on practical sessions, for diverse scientific audiences Demonstrated hands-on experience in developing and applying machine learning or AI methods in a research or applied science context Solid programming skills in Python, including familiarity with ML/DL libraries (e.g. PyTorch, scikit-learn, HuggingFace Transformers) Familiarity with use of large language models or other foundation models as research tools in a life science research context Understanding of responsible AI principles, including fairness, bias, transparency, and research ethics Excellent communication and facilitation skills, with the ability to engage audiences from beginners to domain experts in a multi-disciplinary and multi-cultural context. Strong collaboration skills, e.g. manage projects in collaboration with multiple stakeholders Strong organisational skills and the ability to manage multiple projects independently in a dynamic environment Fluency in spoken and written English You might also have Experience applying AI to biological or biomedical questions (e.g. protein structure prediction, genomics, bioimage analysis) Experience with course development tools an

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