offline

AI in Science: Principles, Practice & Possibilities

When

August 21, 16:00

Format

Offline in Lviv

Registration deadline

August 20

Fee

Free, with prior selection

pc

About

Autonomous offline navigation, sensors that understand the body, and models that help explain complex physical phenomena are real-world use cases of AI in scientific research.

Together with the National University Lviv Polytechnic and the Department of Artificial Intelligence Systems, we’re launching a lecture series titled <AI in Science: Principles, Practice & Possibilities>, where we’ll explore how modern researchers use artificial intelligence in their work.

If you work with physical problems, computational models, or edge devices, join us to learn how it’s done in the UK, discuss real-world use cases, and ask questions directly.
Lectures will be held in English.

Who is it for

  • PhD students
  • Early-career researchers and Master’s students in physics, computer science, applied mathematics, or engineering

To participate

  • Fill in the [registration form]
  • Wait for confirmation

What to expect

  • Exposure to applied approaches in using AI/ML for scientific research and physical problems
  • Discussions of practical case studies: model training, algorithm selection, and working with scientific data
  • A live debate on energy-efficient AI and architectures for edge devices
  • Introduction to neuromorphic computing and its potential applications
  • The opportunity to ask questions to an expert and gain insights from real-world research projects in the UK

Speaker

Dr. Jack Griffiths
Dr. Jack Griffiths

Researcher at the University of Sheffield, working at the intersection of AI and physics

 During his PhD at Durham, Jack applied ML to quantum physics problems, and currently focuses on low-power edge AI and generative models for complex physical processes.

Program

From Data to Discovery: Using AI & ML in Your Research
16:15 – 17:15

Jack will introduce the fundamentals of artificial intelligence (AI) and machine learning (ML) for researchers in physics, engineering, and related fields, using examples from his own work in quantum physics. Participants will learn how to choose appropriate approaches for data analysis, avoid common pitfalls, and take practical first steps — no prior experience with AI required.

Machine Learning at the Edge: Energy-Efficient Learning on Edge Devices
17:30 – 18:30

Jack will discuss current challenges in machine learning on edge devices, particularly the issue of high energy consumption in traditional models. He will present an alternative approach — neuromorphic computing — along with examples of its applications in prosthetics and military technology. The talk will conclude with a discussion of open questions in this emerging field.

Networking session
18:30 – 20:00

Time for discussion, idea exchange, and insights

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