Di seguito è riportato l’elenco delle attività didattiche relative all’a.a. 2026/2027, con indicazione di titolo, docente e syllabus.

Plastic pollution: environmental impacts and monitoring techniques - Lecturer: Gilberto Binda

 Objectives: This class aims to showcase the recent evidence of plastic pollution in the environment, highlighting the analytical challenges for risk assessment and monitoring. 

Program: Plastic pollution is a pervasive environmental issue which has been extensively studied in recent years, while still leaving key knowledge gaps. This course will give an overlook on plastic pollution, with emphasis on plastic emissions and mobility through the environment. It will then assess the current knowledge on plastic environmental aging and (micro)plastic environmental effects, with a focus on the ecological implications in key ecosystems, especially aquatic ecosystems. The course will then summarize the current knowledge and innovative trends in monitoring assessments, including sampling, sample pre-treatments and analytical techniques. 

2 Biodiversity under global change: ecology, adaptation, and conservation - Lecturer: Maria Vittoria Mazzamuto 

Objectives: The course provides an overview of how biodiversity is being affected by human-driven environmental change, considering different levels of organization. It will focus on ecological responses to global change, on how biodiversity can be monitored across taxa, and on how this knowledge is used in conservation, with wildlife systems often serving as practical examples. Alongside the theoretical component, the course is designed to develop practical skills in problem solving and scientific communication through in-class activities. Particular attention is given to interdisciplinary perspectives and to linking ecological concepts with current challenges in biodiversity conservation and management. Programme: Current global environmental change is characterized by widespread and increasing human influence on ecosystems, with clear consequences for biodiversity across systems. The course introduces the main drivers of biodiversity change and discusses how biological systems respond to these pressures at different scales. With a broad perspective that connects species-level responses to patterns at the community and ecosystem levels, students will examine how scientific knowledge can be translated into conservation action with the help of monitoring techniques that are applicable across environmental disciplines. The course will focus on the following main topics: 1. Biodiversity responses to anthropogenic environmental change 2. Non-invasive techniques for monitoring and studying biodiversity 3. Human-biodiversity interactions and coexistence in shared landscapes 4. Translating ecological research into applied conservation strategies Through active participation, students will enhance their ability to reason scientifically, work collaboratively, and apply ecological knowledge within the broader context of environmental research and management. Class Format: Each topic will include a frontal lesson to introduce key concepts, followed by a structured in-class activity that encourages students to engage critically with scientific and management challenges. Activities are designed to be completed during class time and include group discussions and presentations, stakeholder roleplays, data interpretation exercises, and the creation of scientific abstracts or posters. Class will meet for a 2-hour time block for 4 days, for a total of 8 hours.

Teaching materials will be provided by the lecturer.

 Required equipment: Laptop computer, Microsoft Office package (or similar), internet access. 

3 Earthquake environmental effects and seismic hazard - Lecturer: Maria Magdalena Velazquez Bucio 

The course seeks to raise knowledge among PhD students in environmental sciences about seismic landscape characterization and earthquake risk mitigation in tectonically active regions. Objectives: To transmit principles and methodologies in Earthquake Geology and Seismic Landscape analysis, aimed at quantifying seismic hazards, taking advantage from the Environmental Seismic Intensity scale (ESI-07)

 Evaluation: A true/false written test on the fourth day of the course. For its ease of application and correction, and in turn for allowing many aspects transmitted to be easily covered and explored, participants will have to complete a written test of the true-false type at the end of the theoretical course. 

Minimum Contents: Earthquakes and Seismicity, Source Parameters, Magnitude and Intensity, Active Tectonics and Seismic Landscape analysis, Seismic history and seismogenic characterization, Earthquake environmental effects, ESI07 macroseismic scale and intensity assessment, Geological effects of subduction earthquakes (e.g., Pacific coast of Mexico), Normal faulting earthquakes (e.g., Central Italy), Siting of critical facilities, Paleoseismology and geochronology of deformations, Geological record of past earthquakes and tsunamis, (Paleo-) Liquefaction of soils; Paleoseismic methods: Trenches, test pits, cores; Excavation and interpretation of paleoseismic trenches; magnitude of events and their periodicity (seismic history); Seismic Cycle; spatial and temporal clustering of earthquakes.

4  Molecular ecology - Lecturer: Serena Zaccara

Objectives: The goal in this course is to learn and understand principles of ecology and environmental sciences through the appliction of molecular tools. The main Expected learning outcomes are: 

i) analysis of the complexity within interspecific and intraspecific relationships applying the General Theory of Systems 

ii) principles and methods of conservation genetics, introducing population connectivity and its evolutionary implications applied to aquatic and terrestrial species and ecosystems; 

iii) analysis of community ecology based on new technical methods based on NGS and eDNA; 

iv) comparison of sustainability approaches, applied towards ecosystem management. 

Contents: the course will be divided into four parts, for which 2 hours will be dedicated each one. 

i) complexity in ecosystem and The General Systems Theory: lecture will be dedicated to molecular approaches that allow to infer interspecific and intraspecific variability, able to summarize the complexity of ecosystems; 

ii) conservation genetics: Lecture will emphasize the key role of conservation genetics in the development of conservation and management strategies of biodiversity; 

iii) monitoring through highly sensitive detection methods based on eDNA metabarcoding approach: lecture will present a pilot case study on development of protocols for detection of fish community in Italian lake ecosystem; 

iv) sustainability approaches in ecosystem management: case study of fish community vs fisheries activities: lecture will present the position of suitability approach between the two way ecological processes (top-down vs boRom-up). 

Each lecture will present case studies useful to deep into the topics, introducing the last molecular techniques dedicated to. 

Exam: required a short presentation (ppt, about 15 minutes) merging one topics treated with personal scientific interest and own research activities 

5 Microbial deterioration and biorestoration of Cultural Heritage Lecturer: Elisabetta Zanardini

Objectives: Aim of the course is to provide an overview of the microbial processes involved in the biodeterioration and biorestoration of Cultural Heritage (CH) stoneworks.

Methods for the analysis of the microbial communities altering CH stone surfaces and the use of microbial formulation in the biocleaning/biorestoration of CH altered stonework will be described.

Course content: Biodeterioration of CH stonework-Biodeteriogens and mechanisms involved in the biodeterioration processes: chemical, physical and aesthetical damages. Factors influencing the microbial growth and colonisation of CH stone surfaces. Techniques for CH sampling and for the study of the microbial communities: microscopic, culture-dependent and culture-independent methods. New approaches for the control of the biodeterioration processes. Case studies.

Biorestoration of CH stoneworks-Use of microorganisms in the biocleaning/biorestoration of CH altered stoneworks and frescoes. Selection of microorganisms, as biorestoration agents, and of specific carriers for the immobilisation of microbial cells to use in the biotreatments. Lab scale and in situ biotreatments. Case studies.

Course material: Course slides (pdf) and scientific research papers.

6 Sustainable development, circular economy and environmental footprint - Lecturer: Elena Rada

The course aims to provide basic knowledge of sustainable development from a socio-economic and ecological perspective, with particular attention to circular economy models and environmental footprints. The course will integrate interdisciplinary aspects of environmental science, economics, politics, and engineering. 

At the end of the course, the student will be able to:

•critically approach sustainable development, considering the Sustainable Development Goals, (SDGs) and planetary boundaries

•critically evaluate the evolution and limitations of circular economy concepts in different contexts

•understand and consult regulatory frameworks

•integrate environmental criteria, considering environmental footprints, when formulating alternatives

The contents of the course are listed below:

• introduction and evolution of Sustainable Development, Agenda 2030, planetary boundaries

• introduction and evolution of the Circular Economy and Systems Thinking: from linear to closed-loop, EPR, CEAP, EGD, FIT for 55%

• introduction and evolution of environmental footprints: carbon footprint, water footprint, and material footprint

• calculation methodologies applied to the course topics

All the topics listed above have equal weighting and, consequently, will have comparable duration in terms of class hours. The class portion will last 8 hours.

Final Assessment - The exam will consist of an in-depth report on a topic related to the individual student's doctoral research and the course topics.

Course components - Slides of lessons from the e-learning website, technical papers selected by professor and downloadable from e-learning website.

7 Materials for sustainable production of hydrogen - Lecturer: Vladimiro DAL SANTO

Objectives: To provide an overview of the use of hydrogen as an energy carrier and to provide specific information on materials and processes for its production.

Programme: Introduction to hydrogen as a sustainable energy vector (traditional vectors, alternative vectors, sustainable mobility, and stationary systems). Materials for the production, storage and use of hydrogen. Insights on materials for production: i. heterogeneous catalysts for renewables reforming, pyrolysis, partial oxidation; ii. Electro-catalysts for electrolysers (proton and anionic exchange membrane systems).

8 Data Analysis and AI for Scientific Research: introduction - Lecturer: Barbara Giussani

This course provides a comprehensive introduction to the essential chemometrics and statistical tools required to navigate complex scientific datasets. The program is designed to bridge the gap between raw experimental data and actionable insights through a rigorous yet accessible approach to multivariate analysis and in general artificial intelligence applied to scientific data. 

A core tenet of this course is accessibility and reproducibility: all practical sessions will be conducted using R-based, open-source software. This ensures that participants can seamlessly integrate the methods learned during the course into their own research projects without the barrier of expensive proprietary licenses.

By the end of the course, students will be equipped to:

• Visualize high-dimensional data to identify trends and groupings.

• Construct and interpret Principal Component Analysis (PCA) models for data exploration.

• Apply Multivariate Regression techniques to predict properties of interest.

• Set up and analyze a robust Experimental Design (DoE) to optimize laboratory or industrial processes.

List of Topics Covered

• Introduction to Multivariate Analysis: Transitioning from univariate statistics to the multivariate mindset; understanding data structures and matrices.

• Data Preprocessing and Visualization: Techniques for data cleaning, scaling (Autoscaling, Mean Centering), and exploratory plotting to ensure data quality.

• Principal Component Analysis (PCA): Theory of dimensionality reduction, interpretation of Scores and Loadings plots, and outlier detection.

• Basics of Multivariate Regression: Introduction to Multivariate Linear Regression and its limitations; fundamentals of model validation (Internal vs. External validation).

• Foundations of Experimental Design (DoE): Principles of randomization and replication; introduction to Full Factorial and Fractional Factorial designs for screening key experimental factors.   

9 Data Analysis and AI for Scientific Research: advanced level - Lecturer: Barbara Giussani

This advanced course is designed for researchers and practitioners who seek to master complex data architectures and predictive modeling. Building upon foundational knowledge, this module dives into the dynamics of evolving processes and the rigorous validation required for industrial-grade predictive analytics.

The course maintains a strong commitment to reproducible science: all methodologies are implemented using R-based, open-source tools, empowering participants to deploy high-level analytical pipelines directly into their research environments.

By the end of the course, students will be able to:

• Monitor evolving processes and project new samples into existing model spaces.

• Perform advanced outlier detection and signal preprocessing for complex spectral or sensor data.

• Execute variable selection to build parsimonious and robust predictive models.

• Design sophisticated experiments using D-Optimal criteria and perform complex Response Surface Methodology (RSM) optimizations.

List of Topics Covered

• Evolutionary PCA and Model Projection: Using PCA for process monitoring; techniques for projecting "new" or "future" samples into a pre-established model space to detect deviations.

• Advanced Diagnostics and Outlier Detection: Deep dive into the chemistry of residuals; using Hotelling’s T^2(distance within the model) and Q-residuals (distance from the model) to identify abnormal samples.

• Signal Preprocessing for Predictive Modeling: Advanced techniques such as Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), and derivatives (Savitzky-Golay) to remove physical artifacts from chemical signals.

• Variable Selection Strategies: Enhancing model interpretability and performance by identifying the most influential.

• Advanced Experimental Design: Moving beyond standard cubes; understanding D-Optimal designs for irregular experimental regions and constrained factors.

• Optimization and Response Surface Methodology (RSM): Utilizing Central Composite and Box-Behnken designs to map the "experimental landscape" and locate the optimum. 

Participation in the course is subject to knowledge of the concepts covered in the basic course.

10 Advanced Chemical Strategies in Drug Design: Prodrug Approaches and Targeted Conjugation Module A - Lecturer: Silvia Gazzola

The course aims to provide the basis for the rational design and synthesis of prodrugs.

Programme: Recap of key drug design principles (physicochemical properties, ADMET, pharmacokinetics). Introduction to prodrugs, their classification and role in optimizing drug properties. Overview of main synthetic strategies, including functional group derivatization and approaches to modulate solubility, permeability, and stability.

11 Advanced Chemical Strategies in Drug Design: Prodrug Approaches and Targeted Conjugation Module B - Lecturer: Silvia Gazzola

Objectives: The course aims to examine advanced prodrug strategies for drug delivery, focusing on release mechanisms and targeting.

Programme: Design of prodrugs for targeted delivery; Chemical principles of drug release (hydrolytic, enzymatic, and stimuli-responsive linkers). Introduction to modern conjugation methods (e.g., click chemistry) and their application in drug delivery. Selected case studies from recent literature.

Participation in the course is subject to knowledge of the concepts covered in Module A.

12 In silico methods for green toxicology and sustainable chemistry: regulatory use of QSAR models  - Lecturer: Ester Papa

Objectives: The course provides the basic knowledge necessary for the development, validation and application of in silico models based on quantitative structure-activity relationships (QSAR). These approaches are part of the so-called new approach methodologies (NAMs), which are used as alternatives to animal testing at the academic, industrial and regulatory levels, to support chemical safety assessments, the safe by design approach and chemical substitution. Programme: The use of QSAR models for chemical safety assessments: from the REACH Regulation to the Safe and Sustainable Design Framework. QSAR Model Reporting Format (QMRF) and QSAR Prediction Reporting Format (QPRF). In silico tools for predicting properties and activities of chemicals from their molecular structure. Hands on the application of silico tools to profile chemical hazards and properties.

l corso prevede l’acquisizione di 3 CFU mediante la partecipazione a corsi di formazione, certificati da attestati di partecipazione che vadano a coprire le seguenti aree specifiche: Information technology, Intellettual property protection and Funding opportunities

Tali corsi verranno accettati sia se erogati dal Teaching and Learning Center (TLC) e dalla Scuola di Dottorato, sia se erogati da enti esterni di adeguato livello (es. corsi APRE). È comunque opportuno chiedere preventivamente al coordinatore l’adeguatezza del corso che si intende seguire ai fini del riconoscimento dei CFU.

Gli studenti potranno inoltre seguire attività trasversali fornite dal TLC e dalla Scuola di Dottorato. Tali attività, qui riportate, non sono obbligatorie ma facoltative e prevedono il riconoscimento di CFU nel corso della carriera del singolo dottorando. Possono essere consultate al seguente link: https://www.uninsubria.it/formazione/offerta-formativa/dottorati/attivita-trasversali

Il corso prevede inoltre che gli studenti frequentino attività a scelta libera per un minimo di 8 CFU tra quelle erogate dalle Scuole di Dottorato o da altri enti, purché di adeguato livello formativo universitario (terzo livello). L’ammissione delle attività è comunque subordinata alla preventiva approvazione del coordinatore.