Winter School & Symposium 2025

Winter School & Symposium 2025 Announcement

The Zurich-Basel Plant Science Center (PSC) is excited to announce the second edition of our Machine Learning School, following the success of the 2022 Summer School. This Winter Edition, titled "Harnessing Machine Learning for Breakthroughs in Plant and Environmental Sciences," aims to foster a collaborative community of researchers leveraging machine learning in addressing the pressing challenges in Plant and Environmental Sciences. The event is divided into two parts: Winter School Workshops (March 10-12, 2025) and Symposium (March, 13-14, 2025).

Location: ETHZ Center

Winter School & Symposium 2025 Poster  (PDF, 1 MB)

This intensive three-day program is designed for PhD students and postdoctoral researchers eager to deepen their knowledge in machine learning and gain practical, hands-on experience. The Winter School features interactive workshops using real-world datasets, complemented by short seminars addressing current challenges in analyzing plant and environmental data. Participants will learn how ML can address such challenges with an overview of ML tools and algorithms applied to these fields. Space is limited to 30 participants to ensure a personalized learning experience, so early registration is encouraged. To obtain 2 ECTS, participants in the Winter School are also required to attend the Symposium, which will build on workshop learnings and expand opportunities for networking and interdisciplinary knowledge exchange.

WINTER SCHOOL REGISTRATION LINK open until January 15, 2025 when places will be confirmed.

PROGRAM

Monday, March 10, 2025

Introductory Lecture: 
Machine Learning Refresher (Prof. Dr. Jan Dirk Wegner, UZH/ETHZ)

Machine Learning for Predicting Ecosystem Fluxes (Prof. Dr. Benjamin Stocker, UNIBE) – Practical workshop 3h

Plankton Classification: Opportunities, Methods and Pitfalls (Dr. Marco Baity Jesi, EAWAG) – Practical workshop 4h

Tuesday, March 11, 2025

Plant Species Identification from Photos and Local Habitat Conditions (Dr. Philipp Brun, WSL) – Practical workshop 4h

Individual Tree Species and Health Detection Using Deep Learning Model (Xia Zhongyu, ETHZ) – Practical workshop 4h

Wednesday, March 12, 2025

Organ Detection and Semantic Segmentation in Images (PD. Dr. Andreas Hund & Dr. Lukas Roth, ETHZ) – Practical workshop 5h

Plantseg 2.0: Powerful And User-Friendly Plant Tissue Segmentation (Dr. Lorenzo Cerrone UZH) – Practical workshop 4h

ELIGIBILITY FOR REGISTRATION
Registration is primarily open and free of charge for Master's students, Doctoral students and Postdoctoral researchers affiliated with the University of Zurich, ETH Zurich, and the University of Basel, as well as to team members from groups that generously joined our efforts by contributing workshops to the Winter School. External participants may register if additional places are available. Travel and Accommodation: Accommodation during the winter school if needed and travel should be self-organizend and at your own costs.

PREREQUISITES
Participants should have a foundational understanding of programming in R and Python. Completion of the "Introductory Course to Machine Learning for Plant Sciences - Module 1" is recommended. Although the Winter School will start with a brief refresher lecture on ML by Prof. Jan Dirk Wegner, most of the program will concentrate on advanced ML methods and challenges in plant and environmental sciences.

MOTIVATION LETTER REQUIREMENT
As part of the registration process, applicants are required to submit a motivation letter that includes:

1. A list and description of previous training and experience with programming and machine learning (duration of training and practical experience).
2. An outline of their current research project, highlighting how machine learning may contribute to their work.

Open to all interested academics (registration required), the Symposium will focus on ML applications while providing the audience with crutial understanding of the methodology. The Symposium will provide a broader perspective on how ML can advance our understanding and management of environmental challenges, climate adaptation, biodiversity, and precision agriculture.

LINK to the Sympsium 2025 main page