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X-WR-CALNAME:PHD in Industrial Engineering
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X-WR-CALDESC:Events for PHD in Industrial Engineering
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DTSTART:20250101T000000
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DTSTART;TZID=UTC:20260618T080000
DTEND;TZID=UTC:20260618T170000
DTSTAMP:20260422T025140
CREATED:20260413T152828Z
LAST-MODIFIED:20260413T152828Z
UID:3273-1781769600-1781802000@academics.dii.unipd.it
SUMMARY:Statistics for Engineers
DESCRIPTION:Course contentsThis course provides a robust foundation in statistical methodologies and data-driven modeling tailored for engineering research. The programme bridges traditional inferential statistics with modern computational intelligence\, equipping doctoral candidates with the analytical framework required to extract meaningful insights from complex experimental and industrial datasets. \nThe curriculum is structured to guide students from foundational descriptive and inferential statistics to more advanced analytical techniques. Initial modules cover linear and non-linear regression models\, providing the basis for characterizing system behavior. A significant portion of the course is dedicated to Machine Learning\, specifically investigating both supervised and unsupervised algorithms for classification\, clustering\, and predictive modeling. Additionally\, the programme explores the Design of Experiments (DOE)\, teaching students how to optimize experimental setups and minimize resource expenditure while maximizing data quality. Through hands-on activities with specialized software\, students will learn to apply these theoretical concepts to real-world scenarios\, culminating in the development of a personal statistical project that integrates the methodologies explored during the lectures with their own doctoral research objectives. \nLearning goalsBy the conclusion of the course\, PhD students will have acquired the fundamental statistical concepts necessary to manage diverse data sources\, including those generated within their specific research projects. Participants will develop the proficiency to build and solve physics-based and data-driven systems\, mastering both classical inferential methods and advanced machine learning algorithms. Furthermore\, the course fosters the ability to use professional\, user-friendly statistical software to design\, execute\, and interpret independent research projects. \nSuggested readingsMaterials (slides\, datasets\, etc.) of the course will be provided by the course leaders. \nNotesThe course is structured into 2 online (February) and a Summer School of 4 days (June). The Summer School will take place in Villa San Giuseppe\, Monguelfo\, Bolzano province. During the course an introduction to the use of the following statistical software will be presented: \n\nR and BlueSky\, both open-source software.\nMINITAB\, licensed to University of Padova.
URL:https://academics.dii.unipd.it/phd/event/statistics-for-engineers/
LOCATION:Sede-V\, via Venezia 1\, Padova\, Padova\, 35131\, Italy
CATEGORIES:Event
ATTACH;FMTTYPE=image/jpeg:https://academics.dii.unipd.it/phd/wp-content/uploads/sites/58/2026/04/CDII_statistics-scaled.jpeg
ORGANIZER;CN="PhD Course in Industrial Engineering":MAILTO:dottorato.dii@unipd.it
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DTSTART;TZID=UTC:20260618T100000
DTEND;TZID=UTC:20260618T120000
DTSTAMP:20260422T025140
CREATED:20260421T152411Z
LAST-MODIFIED:20260421T152411Z
UID:3331-1781776800-1781784000@academics.dii.unipd.it
SUMMARY:Green Chemistry and Technology – day 4
DESCRIPTION:Course contentsThis course explores the fundamental principles of sustainable chemistry and engineering\, focusing on the design of products and processes that minimize environmental impact. The programme bridges theoretical foundations with industrial applications\, providing doctoral candidates with the methodologies required to evaluate and implement “green” innovation in chemical production. \nThe curriculum provides an introduction to green chemistry and sustainable technology\, examining the relationship between chemical engineering and environmental preservation. Key topics include the principles of green nanotechnology—focusing on nanomaterials derived from green sources—and sustainable approaches to energy production\, such as fuel cells. A significant portion of the programme is dedicated to Life Cycle Assessment (LCA) and the use of green metrics to evaluate the environmental performance of chemical processes. Through laboratory sessions\, students will gain hands-on experience in calculating the “greenness” of products\, ensuring they are equipped with the quantitative tools necessary to support sustainable industrial workflows. \nLearning goalsBy the conclusion of the course\, students will master the core principles of green chemistry and engineering. Participants will acquire the analytical capability to perform preliminary assessments of the “greenness” of materials and processes. The goal is to develop a critical understanding of sustainable development\, enabling future researchers to design chemical systems that are both functionally optimized and environmentally responsible. \nReadingsAnastas\, P. T.; Warner\, J. C.; Green Chemistry: Theory and Practice; Oxford University Press: New York\, 2000 (available at Biblioteca Centrale di Ingegneria) \nLancaster\, M.; Green Chemistry: an introductory text; Royal Society of Chemistry: Cambridge\, 2010 (available at Biblioteca Centrale di Ingegneria) \nJiménez-González\, C.C.; Constable\, D.; Green chemistry and engineering: a practical design approach; Wiley: Hoboken\, New Jersey\, 2011 (available online at https://galileodiscovery.unipd.it) \nBenvenuto\, Mark A.\, editor.; Ruger\, George\, editor; Green chemistry and technology; 2021; Berlin; Boston: De Gruyter (available online at https://galileodiscovery.unipd.it) \nMcKeag\, Thomas\, Green chemistry in practice: greener material and chemical innovation through collaboration\, 2023; Kidlington\, England; Cambridge\, MA: Elsevier (available online at https://galileodiscovery.unipd.it) \nTiwari\, Vinod K.\, Tiwari\, Vinod K.\, Green chemistry: introduction\, application and scope\, 2022; 1st ed. 2022; Singapore: Springer (available online at https://galileodiscovery.unipd.it) \nThe lectures’ slides will be made available to all the participants.
URL:https://academics.dii.unipd.it/phd/event/green-chemistry-and-technology-day-4/
LOCATION:On-line
CATEGORIES:Event
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ORGANIZER;CN="PhD Course in Industrial Engineering":MAILTO:dottorato.dii@unipd.it
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