Lab Automation can reduce manual tasks and speed up repetitive processes by up to 80%.
However, leading the transition towards technology-enabled labs requires the right skills, knowledge and experience to manage resistance to change, select and implement the right technology, analyze meaningful data, and overcome legacy issues with aging infrastructure.
Join industry experts in San Diego in November as they collaboratively share insights to accelerate the transformation of life science, pharma, and biotech R&D through emerging technologies.
Expert Future Lab Automation & Technology 2023 Speakers
This summit will provide you with the necessary tools to:
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- Develop an effective roadmap to accelerate the transition towards smart, connected, technology-enabled labs
- Prepare your lab for digital transformation and develop a workforce culture that embraces technology and innovation
- Improve cross-departmental and external partnerships communication to streamline operations, align strategic objectives and increase project success
- Effectively manage, store, and analyze big data generated by automated laboratory technology to improve the speed and accuracy of decision-making
- Utilize equipment and resources to maximize efficiency, minimize human errors, and reduce costs
- Optimize laboratory workflows by effectively integrating automation technology to accelerate R&D and enhance flexibility, productivity, and accuracy of scientific data
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Who should attend:
Future Labs, Automation & Technology US 2023 is designed for VP’s, Directors, Heads and Managers from:
- Automation / Robotics
- R&D
- Lab IT/ Research IT
- Informatics
- Technology & Innovation
- Quality Assurance / Quality Control
- Laboratory Operations & Strategy
- Regulation, Compliance & Validation
- Data & Analytics
- Industry 4.0 / Lab 4.0
- Emerging Technology / Artificial Intelligence (AI) / Machine Learning (ML
Our sponsors include technology, service & solution providers specializing in:
- Laboratory Automation / Lab Digitalization
- Robotics
- Informatics
- Data & Analytics
- Artificial Intelligence (AI) / Machine Learning (ML)
- Cloud Computing
- IoT Hardware, Software, Platforms, Infrastructure
- Cybersecurity