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As the life science industry is increasingly relying on AI-enabled applicant tracking systems (ATS) and skills-based screening, CVs are now being evaluated by algorithms
This workshop helps life science professionals understand how AI scans, ranks and filters CVs and how to present their experience and skills in a way that is both machine-readable and scientifically credible.
The focus is on optimising CVs without diluting scientific depth or relevance.
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This workshop will cover:
How AI and ATS read CVs:
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Key word extraction, skills inference, role similarity and experience weighting.
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Common failure points for CVs e.g. PDF formatting, dense publications lists etc.
Structuring life science CVs for machine readability
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Clear section hierarchy (skills, experience, methods, tools etc.)
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Avoiding layouts that break AI parsing (tables, graphics etc.)
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Translating academic and industry experience into AI-recognizable signals.
Desirable skillsets
AI, digital and data literacy for non-AI roles
How to present publications, patents, and posters in an AI-screened CV