Szkoła letnia "Advanced Measurement Methods with Smart Sensing Using Machine Learning" | FTiMS - Politechnika Gdańska

This summer school focuses on advanced experimental measurement techniques in physics and engineering, emphasizing smart sensing and modern machine learning (ML) methods. You will learn to make the most of experimental data—including negative or inconclusive findings often overlooked in publications. We will demonstrate how ML can reveal hidden patterns and drive scientific progress.

This intensive course is jointly organised by the Faculty of Applied Physics and Mathematics of Gdańsk University of Technology (Poland) and the Department of Physical & Environmental Sciences of Texas A&M University-Corpus Christi (USA), and the MathWorks company (creators of MATLAB and Simulink).

 It is open to all ENHANCE member universities.

The summer school “Advanced Measurement Methods with Smart Sensing Using Machine Learning” is a BIP course designed for:

• Postdocs, Ph.D. students, and M.Sc. students in STEM fields (particularly physics, chemistry, engineering, and environmental sciences).
• Scientists or researchers seeking to understand and implement current Machine Learning tools for experimental data analysis.
• Anyone aiming to explore unexamined or complex datasets requiring advanced analytical techniques.

• Venue for the in-person part: Gdańsk University of Technology
• Dates: July 5 – July 14, 2025

• Keynote Speakers (Proposed):

1. Dawid Gradolewski (Bioseco)
2. Przemek Majewski (DLabs.AI)
3. Intel Gdańsk Division

• MATLAB & ML/LLM Sessions: Conducted by MathWorks (Polish & US branches) with PJ, MD, MC, PO, JR.
• Business Day: May involve industry partners (GKB, PKB+) for networking.
• Schedule may shift due to speaker availability, lab capacity, or logistical needs.

What Is Provided

During and after the summer school, participants will have access to:

• Coffee during designated coffee breaks
• Laboratory sessions and lectures
• A sightseeing trip to Gdańsk and surrounding areas
• Academic Certificate
• Networking opportunities with fellow scientists and businesses seeking ML expertise
• The opportunity to showcase personal or group ML data analysis results developed during the course

Expected learning outcomes

The expected learning outcomes are related to the following learning objectives:

1. Identify limitations and biases in current physical measurement approaches.
2. Perform advanced experiments and collect high-quality data.
3. Employ ML techniques to analyze complex datasets.
4. Understand the value of negative or inconclusive results in scientific research.

Prerequisites

Before starting the classes, you must complete the introductory interactive MATLAB online course and pass the test:

• free, self-paced MATLAB Onramp course:

https://matlabacademy.mathworks.com/details/matlab-onramp/gettingstarted

Learning Opportunity Structure

1. Before starting the classes, you must complete the introductory interactive MATLAB online course and pass the test.
2. Lectures (18 hrs): Fundamentals of measurement methods, smart sensing, ML theory
3. Labs & Workshops (22 hrs): Group-based (1–4 students) experiments and ML applications
4. Assignments: Reading-based homework and project tasks
5. Final Data Analysis: Individual ML-based analysis
6. Final Presentation (Pass/Fail):

• Present your research or
• Demonstrate summer school methods applied to your academic/professional interests

Type of Assessment

Formal. Presentation of the prepared projects for business representatives (Pass/Fail).

Quality Assurance

According to the quality assurance standards of Gdańsk Tech and Texas A&M University-Corpus Christi.

Grading System

• Group Lab Work: Shared group grade
• Individual Data Analysis: Individual grade based on ML application
• Final Presentation: Pass/Fail, based on clarity, depth, relevance

Permit AI with Conditions

• AI tools (e.g., ChatGPT) may support learning but not replace critical thinking and analysis.
• Cite any AI-generated text or insights.
• Instructors may request an oral defense of submitted work to ensure authentic mastery.

US Final Grade Scale

Class Average (X) Grade 

X > 90.0% A – Excellent

 80% ≤ X ≤ 90.0% B – Good

 70% ≤ X < 80.0% C – Satisfactory

 60% ≤ X < 70.0% D – Passing

X < 60.0% F – Failing

EUR Final Grade Scale

Class Average (X) Grade 

X > 90.0%  A

 82% ≤ X ≤ 90.0% B

 76% ≤ X < 82.0% C

 68% ≤ X < 76.0% D

 60% ≤ X < 68.0% E

 X < 60.0% F – Failing

Estimated effort

Format & Workload

• Total supervised instruction hours: 50
• Lectures: 18 hours
• Labs/Workshops: 22 hours
• Seminars/presentations: 10 hours
• Introductory interactive online course with two tests: 8 hours
• Language: English
• Significant outside study (readings, project work) required

Schedule Overview

Saturday, July 5, 2025

• 09:00–11:30: (No scheduled events)
• 11:30: Coffee Break
• 12:00: Introduction to Measurements, Sensors, and Data Acquisition
• 13:30: Lunch
• 15:00–18:00: Arrival & Reception

Sunday, July 6, 2025

• 09:00–11:30: (No scheduled events)
• 10:00: Introduction to Gdańsk Tech Facilities & Welcome Tour (RB)
• 11:30: Coffee Break
• 12:00: (No scheduled event)
• 13:30: Lunch
• 15:00–18:00: Social Event / Meeting with Gdańsk Tech PhD Students

Monday, July 7, 2025

• 09:00: Keynote Lecture 1 (e.g., Dawid Gradolewski, Bioseco)
• 10:00: Introduction to Data Engineering (PJ)
• 11:30: Coffee Break
• 12:00: Fundamentals of ML & Data Science (PJ)
• 13:30: Lunch
• 15:00–18:00: Introduction to Computer Lab (PJ, MD) parallel: Introduction to Measurements Lab (MC, PO)

Tuesday, July 8, 2025

• 09:00: Keynote Lecture 2 (e.g., Przemek Majewski, DLabs.AI)
• 10:00: Structure of Measurement Systems; Electrical & Magnetic Measurements
• 11:30: Coffee Break
• 12:00: MATLAB (Day 1) – Intro to ML/LLM Data Analysis
• 13:30: Lunch
• 15:00–18:00: MATLAB Lab Activity (Day 1) – ML & LLM; parallel: Measurements Lab

Wednesday, July 9, 2025

• 09:00: Keynote Lecture 3 (e.g., Intel Gdańsk)
• 10:00: Sensors (JR)
• 11:30: Coffee Break
• 12:00: MATLAB (Day 2) – ML & LLM Data Analysis
• 13:30: Lunch
• 15:00–18:00: MATLAB Lab Activity (Day 2)

Thursday, July 10, 2025

• 09:00: Keynote Lecture 4
• 10:00: Smart Sensing (JR)
• 11:30: Coffee Break
• 12:00: MATLAB (Day 3)
• 13:30: Lunch
• 15:00–18:00: MATLAB Lab Activity (Day 3)

Friday, July 11, 2025

• 09:00: (No scheduled event)
• 10:00: Workshop: Hands-on Data & ML
• 11:30: Coffee Break
• 12:00: (No scheduled event)
• 13:30: Lunch
• 15:00–18:00: Closing Ceremony

Saturday, July 12, 2025

• 09:00: (No scheduled event)
• 10:00: Gdańsk Sightseeing Tour
• 11:30: Coffee Break
• 12:00: (No scheduled event)
• 13:30: Lunch
• 15:00–18:00: Open/Free Time

Sunday, July 13, 2025

• 09:00: (No scheduled event)
• 10:00: Final Student Presentations
• 11:30: Coffee Break
• 12:00: (No scheduled event)
• 13:30: Lunch
• 15:00–18:00: Open/Free Time

Monday, July 14, 2025

• 09:00: (No scheduled event)
• 10:00: Business Day (e.g., with Dorota Sobieniecka (GKB), Tomasz Klajbor (PKB+))
• 11:30: Coffee Break
• 12:00: (No scheduled event)
• 13:30: Lunch
• 15:00–18:00: Possible Business Day Follow-Up / Wrap-up

 

How to enroll

Application Requirements

Before applying to the summer school “Advanced Measurement Methods with Smart Sensing Using Machine Learning” please prepare and submit the following:

1) Recent CV

• Highlight your academic background, relevant research/work experience, and pertinent technical skills.

2) Reference Letter

• Provide the reference’s email address and phone number for verification.

3) Half-Page Statement of Purpose

• Explain why you want to attend the summer school and how it aligns with your academic or professional goals.

4) Proof of MATLAB Proficiency

• A PDF certificate verifying completion of the free, self-paced MATLAB Onramp course:
• https://matlabacademy.mathworks.com/details/matlab-onramp/gettingstarted

Submit required documents 1- 3 (see “Application Requirements”) by 31 May 2025, through https://forms.pg.edu.pl/01JNGVG8XBR5HQJ5GP1Q7T7176

Submit Proof of MATLAB Proficiency by 10 June 2025, through https://forms.pg.edu.pl/01JN4CEWVM06G0MKDGPF8BHEGC

Further Information

Participation in the course is free of charge. Each participant should cover the costs of travel, accommodation, and meals. Accommodation in Gdańsk Tech dormitories is provided (around 15 EUR/night).

Students from all ENHANCE universities are warmly welcome to participate in this BIP. For more information on possible funding opportunities at your home university, please contact your local ENHANCE Mobility/Education Officer or International office. You can also check your university website for more information. As a student from TU Delft you are warmly welcome to apply for this offer, but please read the important information about TUDelft possibilities for scholarships and recognition here: https://brightspace-cc.tudelft.nl/course/25049/enhance-community

Contact: dr hab. inż. Ryszard Barczyński, prof. PG jbarcz@pg.edu.pl