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Advancing Data Technologies to Corner AMR 2025 – Early bird

Advancing Data Technologies to Corner AMR 2025 - Early bird

€195.00

Event Advancing Data Technologies to Corner AMR 2025
Date 19/20 June, 2025 (two half days)
Time 13:30 AM – 17:30 PM CEST (both days)
Included √ Full Symposium: Lectures – Matchmaking – Round Table
√ Inclusion in the searchable Technology Database
√ Own Technology Page
√ Enrollment in online Matchmaking program
Full price € 495

Category:

Description

Join Us for “Advancing Data Technologies to Corner AMR 2025” – June 19-20

Step into the future of fighting Antimicrobial Resistance (AMR) at the 6th AMR Insights International Matchmaking Symposium ADTCA. This dynamic online event highlights groundbreaking data technologies, innovative products, and transformative services poised to make a global impact in curbing AMR.

Why Attend?

  • Inspiring Keynotes: Hear from thought leaders driving the future of AMR solutions.
  • Orchestrated Matchmaking: Connect with like-minded innovators, industry experts, and key stakeholders.
  • Round table Discussions & Q&A: Dive deep into the challenges and opportunities shaping AMR strategies.
  • Networking Opportunities: Build lasting collaborations to fuel your impact on AMR.

Join the movement to revolutionize the fight against AMR—register now and make a difference!

Additional information

Data Technology:

Below are the top 5 data technologies instrumental in combating antimicrobial resistance (AMR). Each technology is accompanied by its relevance and key application areas:
Artificial Intelligence (AI)

Relevance: Central to AMR research for pattern recognition, decision-making, and predictive analytics
Applications: Predicting resistance patterns, discovering novel antimicrobials, optimizing treatments
Machine Learning (ML)

Relevance: Analyzing vast datasets, detecting resistance genes, and predicting trends.
Applications: Resistance gene detection, analyzing genomic data, predicting trends.
Deep Learning (DL)

Relevance: Handling large-scale genomic and phenotypic datasets.
Applications: Identifying new drug targets, analyzing complex interactions.
Bioinformatics

Relevance: Processing genomic, proteomic, and metagenomic data
Applications: Analyzing resistance genes in microbial genomes and microbiomes.
Big Data Analytics

Relevance: Managing and extracting insights from massive datasets.
Applications: Identifying AMR hotspots, global surveillance, and policy planning.

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