Hybrid Conferencee

International Conference on Applied Machine Learning for Scientific Applications (ICAML-SA - 26)

3rd - 4th August 2026 | Vienna, Austria

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Conference Notifications:

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Call for Papers Extended:
"The deadline for full paper submissions has been extended for the Research Plus International Conference in Vienna. Submit your research by today to participate in one of the top conferences."
Certificate of Presentation:
"Present your research and receive a Certificate of Presentation to recognise your valuable contribution to the conference."
Abstract Submissions Open:
"Abstract submissions for the Vienna event are now open! Don’t miss the chance to present your research. Submit now."
Networking with Global Experts:
"Engage with researchers and professionals from around the world at the Vienna conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in Vienna, featuring global leaders and innovators sharing their knowledge."
Best Paper & Best Paper Presentation Award:
"Submit your paper and stand a chance to win the Best Paper Presentation Award. The winner will be recognized at the conference in Vienna."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Call for Paper

The ICAML-SA aims to explore emerging trends and future directions in research and innovation. It provides a collaborative platform for researchers and professionals to share ideas that shape the future of their respective domains.

The conference highlights advancements in Computational Science,Data Science, encouraging innovative, solution-oriented research that addresses global challenges and technological evolution.

Authors are invited to submit papers addressing, but not limited to, the following areas:

01
Applied machine learning in scientific fields
02
Case studies of ML in scientific research
03
Real-world applications of machine learning
04
Machine learning for experimental data analysis
05
AI techniques for scientific modeling
06
Data-driven decision-making in science
07
Machine learning for predictive maintenance
08
Applications of ML in environmental science
09
Machine learning in social science research
10
AI for optimizing scientific workflows
11
Challenges in applying ML to science
12
Ethics of machine learning applications
13
Machine learning for data-driven discoveries
14
AI in computational biology applications
15
Interdisciplinary approaches to applied ML
16
Machine learning for sensor data analysis
17
AI for enhancing research reproducibility
18
Future trends in applied machine learning
19
Collaborative research using machine learning
20
Machine learning for scientific visualization