Search result: Catalogue data in Autumn Semester 2024
Science, Technology, and Policy Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 Minor in Natural Sciences and Engineering | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data and Computer Science | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 263-2400-00L | Reliable and Trustworthy Artificial Intelligence | W | 6 credits | 2V + 2U + 1A | M. Vechev | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Creating reliable, secure, robust, and fair machine learning models is a core challenge in artificial intelligence and one of fundamental importance. The goal of the course is to teach both the mathematical foundations of this new and emerging area as well as to introduce students to the latest and most exciting research in the space. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Upon completion of the course, the students should have mastered the underlying methods and be able to apply them to a variety of engineering and research problems. To facilitate deeper understanding, the course includes a group coding project where students will build a system based on the learned material. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course is split into 4 parts: Robustness of Machine Learning -------------------------------------------- - Adversarial attacks and defenses on deep learning models. - Automated certification of deep learning models (major trends: convex relaxations, branch-and-bound, randomized smoothing). - Certified training of deep neural networks (combining symbolic and continuous methods). Privacy of Machine Learning -------------------------------------- - Threat models (e.g., stealing data, poisoning, membership inference, etc.). - Attacking federated machine learning (across vision, natural language and tabular data). - Differential privacy for defending machine learning. - AI Regulations and checking model compliance. Fairness of Machine Learning --------------------------------------- - Introduction to fairness (motivation, definitions). - Enforcing individual fairness (for both vision and tabular data). - Enforcing group fairness (e.g., demographic parity, equalized odds). Robustness, Privacy and Fairness of Foundation Models --------------------------------------------------------------------------- - We discuss all previous topics, as well as programmability, in the context of latest foundation models (e.g., LLMs). More information here: https://www.sri.inf.ethz.ch/teaching/rtai24. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | While not a formal requirement, the course assumes familiarity with basics of machine learning (especially linear algebra, gradient descent, and neural networks as well as basic probability theory). These topics are usually covered in “Intro to ML” classes at most institutions (e.g., “Introduction to Machine Learning” at ETH). The coding project will utilize Python and PyTorch. Thus some programming experience in Python is expected. Students without prior knowledge of PyTorch are expected to acquire it early in the course by solving exercise sheets. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 263-3845-00L | Data Management Systems | W | 8 credits | 3V + 1U + 3A | G. Alonso | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course will cover the implementation aspects of data management systems using relational database engines as a starting point to cover the basic concepts of efficient data processing and then expanding those concepts to modern implementations in data centers and the cloud. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal of the course is to convey the fundamental aspects of efficient data management from a systems implementation perspective: storage, access, organization, indexing, consistency, concurrency, transactions, distribution, query compilation vs interpretation, data representations, etc. Using conventional relational engines as a starting point, the course will aim at providing an in depth coverage of the latest technologies used in data centers and the cloud to implement large scale data processing in various forms. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course will first cover fundamental concepts in data management: storage, locality, query optimization, declarative interfaces, concurrency control and recovery, buffer managers, management of the memory hierarchy, presenting them in a system independent manner. The course will place an special emphasis on understating these basic principles as they are key to understanding what problems existing systems try to address. It will then proceed to explore their implementation in modern relational engines supporting SQL to then expand the range of systems used in the cloud: key value stores, geo-replication, query as a service, serverless, large scale analytics engines, etc. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The main source of information for the course will be articles and research papers describing the architecture of the systems discussed. The list of papers will be provided at the beginning of the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course requires to have completed the Data Modeling and Data Bases course at the Bachelor level as it assumes knowledge of databases and SQL. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 263-5902-00L | Computer Vision | W | 8 credits | 3V + 1U + 3A | M. Pollefeys, S. Tang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The goal of this course is to provide students with a good understanding of computer vision and image analysis techniques. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The objectives of this course are: 1. To introduce the fundamental problems of computer vision. 2. To introduce the main concepts and techniques used to solve those. 3. To enable participants to implement solutions for reasonably complex problems. 4. To enable participants to make sense of the computer vision literature. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Camera models and calibration, invariant features, Multiple-view geometry, Model fitting, Stereo Matching, Segmentation, 2D Shape matching, Shape from Silhouettes, Optical flow, Structure from motion, Tracking, Object recognition, Object category recognition | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | It is recommended that students have taken the Visual Computing lecture or a similar course introducing basic image processing concepts before taking this course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-3005-00L | Natural Language Processing  | W | 7 credits | 3V + 3U + 1A | R. Cotterell | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course presents topics in natural language processing with an emphasis on modern techniques, primarily focusing on statistical and deep learning approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The objective of the course is to learn the basic concepts in the statistical processing of natural languages. The course will be project-oriented so that the students can also gain hands-on experience with state-of-the-art tools and techniques. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Lectures will make use of textbooks such as the one by Jurafsky and Martin where appropriate, but will also make use of original research and survey papers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 263-5057-00L | From Publication to the Doctor's Office The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 3 credits | 2S + 1A | O. Demler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This seminar course is designed to provide students with an opportunity to review and critically evaluate recent publications in medical field focusing on examples when CS method or bioinformatics/statistical technique has lead to an instrumentation, technique or drug approved for clinical practice use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Throughout the course, students will read and analyze recent publications that demonstrate successful applications and sometimes failures in medicine. Promissing research applications will also be duscussed. The publications will cover a wide range of topics, including drug discovery, image analysis, prognostic models, and learning healthcare. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course will be structured as half lecture content and half seminar content. Lectures will review state of the medical practice prior to the discovery, obstacles in moving the field forward, and the need for improvement. Lecture will be followed by the seminar part where students will take turns presenting the assigned publications and leading the discussions. Students‘ presentations will focus on the main findings, and specfic steps taken to translate the finding into clinical practice. Publications will include examples of: • specific CS/bioinformatics/statistics applications that has been brought to „bedside“ – has been approved by European Medicines Agency / Food and Drug Administration (USA) for clinical use or are widely used in medical research; • examples of failures of how a discovery did not translate into an endproduct and why; current active research areas. Covered topics will include some of the following: • Drug discovery: Computer-aided drug discovery has become an integral part of the drug development process, enabling researchers to design and screen large libraries of molecules in silico (i.e., using computer simulations) before synthesizing and testing them in the lab. This has led to the discovery of new drug candidates for a wide range of diseases, including cancer, Alzheimer's disease, and HIV/AIDS. • Genomics: Advances in computational genomics have enabled researchers to analyze and interpret large-scale genomic data, including DNA sequencing data, to identify disease-causing mutations, genetic risk factors, and drug targets. Examples of the development of personalized medicine, where treatments are tailored to an individual's genetic makeup. Examples when drug target identified by genetics has led to approved treatment. • Imaging: Computer vision and image processing techniques have revolutionized medical imaging, enabling researchers to extract quantitative information from medical images that were previously inaccessible. This has led to the development of new diagnostic and prognostic tools for a wide range of diseases, including cancer, cardiovascular disease, and neurological disorders. • Real-world data applications: emulation of clinical trials using electronic health records data. • Large language models: Generating clinical trial protocols using large language models. Natural Language Processing for information extraction and interpretation. • Learning healthcare systems: Advances in data analytics and information technology have enabled the development of learning healthcare systems, which use real-time data from electronic health records, medical devices, and other sources to improve patient outcomes and reduce healthcare costs. This has the potential to transform the way healthcare is delivered, making it more personalized, efficient, and effective. In addition to the presentations, students will also be required to write critical reviews of the assigned publications throughout the course. The reviews will be evaluated based on the students' ability to identify the strengths and weaknesses of the publications and to provide insightful and constructive feedback. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course is intended for advanced undergraduate and graduate students with a background in computer science, bioinformatics, or a related field and interest in applying their skills to medical research. This course assumes a working knowledge of R/Python and intermediate statistical analysis, including linear, logistic, survival regressions or ability and interest to learn them outside of the class. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Life Science and Health | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 376-0021-00L | Materials and Mechanics in Medicine | W | 4 credits | 3G | M. Zenobi-Wong, J. G. Snedeker | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Understanding of physical and technical principles in biomechanics, biomaterials, and tissue engineering as well as a historical perspective. Mathematical description and problem solving. Knowledge of biomedical engineering applications in research and clinical practice. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Understanding of physical and technical principles in biomechanics, biomaterials, tissue engineering. Mathematical description and problem solving. Knowledge of biomedical engineering applications in research and clinical practice. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Biomaterials, Tissue Engineering, Tissue Biomechanics, Implants. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | course website on Moodle | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Introduction to Biomedical Engineering, 3rd Edition 2011, Autor: John Enderle, Joseph Bronzino, ISBN 9780123749796 Academic Press | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 376-1103-00L | Frontiers in Nanotechnology | W | 4 credits | 4V | V. Vogel, further lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Many disciplines are meeting at the nanoscale, from physics, chemistry to engineering, from the life sciences to medicine. The course will prepare students to communicate more effectively across disciplinary boundaries, and will provide them with deep insights into the various frontiers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Building upon advanced technologies to create, visualize, analyze and manipulate nano-structures, as well as to probe their nano-chemistry, nano-mechanics and other properties within manmade and living systems, many exciting discoveries are currently made. They change the way we do science and result in so many new technologies. The goal of the course is to give Master and Graduate students from all interested departments an overview of what nanotechnology is all about, from analytical techniques to nanosystems, from physics to biology. Students will start to appreciate the extent to which scientific communities are meeting at the nanoscale. They will learn about the specific challenges and what is currently “sizzling” in the respective fields, and learn the vocabulary that is necessary to communicate effectively across departmental boundaries. Each lecturer will first give an overview of the state-of-the art in his/her field, and then describe the research highlights in his/her own research group. While preparing their Final Projects and discussing them in front of the class, the students will deepen their understanding of how to apply a range of new technologies to solve specific scientific problems and technical challenges. Exposure to the different frontiers will also improve their ability to conduct effective nanoscale research, recognize the broader significance of their work and to start collaborations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Starting with the fabrication and analysis of nanoparticles and nanostructured materials that enable a variety of scientific and technical applications, we will transition to discussing biological nanosystems, how they work and what bioinspired engineering principles can be derived, to finally discussing biomedical applications and potential health risk issues. Scientific aspects as well as the many of the emerging technologies will be covered that start impacting so many aspects of our lives. This includes new phenomena in physics, advanced materials, novel technologies and new methods to address major medical challenges. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | All the enrolled students will get access to a password protected website where they can find pdf files of the lecture notes, and typically 1-2 journal articles per lecture that cover selected topics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 376-1714-00L | Biocompatible Materials | W | 4 credits | 3V | K. Maniura, M. Rottmar, M. Zenobi-Wong | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to molecules used for biomaterials, molecular interactions between different materials and biological systems (molecules, cells, tissues). The concept of biocompatibility is discussed and important techniques from biomaterials research and development are introduced. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course covers the follwing topics: 1. Introdcution into molecular characteristics of molecules involved in the materials-to-biology interface. Molecular design of biomaterials. 2. The concept of biocompatibility. 3. Introduction into methodology used in biomaterials research and application. 4. Introduction to different material classes in use for medical applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Introduction into natural and polymeric biomaterials used for medical applications. The concepts of biocompatibility, biodegradation and the consequences of degradation products are discussed on the molecular level. Different classes of materials with respect to potential applications in tissue engineering, drug delivery and for medical devices are introduced. Strong focus lies on the molecular interactions between materials having very different bulk and/or surface chemistry with living cells, tissues and organs. In particular the interface between the materials surfaces and the eukaryotic cell surface and possible reactions of the cells with an implant material are elucidated. Techniques to design, produce and characterize materials in vitro as well as in vivo analysis of implanted and explanted materials are discussed. A link between academic research and industrial entrepreneurship is demonstrated by external guest speakers, who present their current research topics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Handouts are deposited online (moodle). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Literature: - Biomaterials Science: An Introduction to Materials in Medicine, Ratner B.D. et al, 3rd Edition, 2013 - Comprehensive Biomaterials, Ducheyne P. et al., 1st Edition, 2011 (available online via ETH library) Handouts and references therin. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 376-0300-00L | Essentials in Translational Science | W | 3 credits | 2G | J. Goldhahn, N. K. Brasier, D. Schaffarczyk | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Translational science is a cross disciplinary scientific research that is motivated by the need for practical applications that help people (e.g. Medicines). The course should help to clarify basics of translational science, illustrate successful applications and enable students to integrate key features into their future projects. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After completing this course, students will be able to understand: Principles of translational science including medical device development, intellectual property, regulatory environment and project management Students should be able to apply this knowledge in drug development programs in Pharma, Biotech or their own spin-off. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | What is translational science and what is it not Including: How to identify need? How to choose the appropriate research type and methodology How to measure success? How are medical devices developed? How to handle IP in the development process? How does the regulatory environment impact innovation? How to manage complex development projects? Positive and negative examples will be illustrated by distinguished guest speakers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Principles of Biomedical Sciences and Industry Translating Ideas into Treatments https://doi.org/10.1002/9783527824014 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | 4x online input lecture followed by case preparation and symposium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 752-6105-00L | Epidemiology and Prevention | W | 3 credits | 2V | M. Puhan, R. Heusser | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The module „Epidemiology and prevention“ describes the process of scientific discovery from the detection of a disease and its causes, to the development and evaluation of preventive and treatment interventions and to improved population health. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The overall goal of the course is to introduce students to epidemiological thinking and methods, which are criticial pillars for medical and public health research. Students will also become aware on how epidemiological facts are used in prevention, practice and politics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The module „Epidemiology and prevention“ follows an overall framework that describes the course of scientific discovery from the detection of a disease to the development of prevention and treatment interventions and their evaluation in clinical trials and real world settings. We will discuss study designs in the context of existing knowledge and the type of evidence needed to advance knowledge. Examples from nutrition, chronic and infectious diseases will be used in order to show the underlying concepts and methods. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 752-6151-00L | Public Health Concepts | W | 3 credits | 2V | R. Heusser | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The module "public health concepts" offers an introduction to key principles of public health. Students get acquainted with the concepts and methods of epidemiology. Students also learn to use epidemiological data for prevention and health promotion purposes. Public health concepts and intervention strategies are presented, using examples from infectious and chronic diseases. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | At the end of this module students are able: - to interpret the results of epidemiological studies - to critically assess scientific literature - to know the definition, dimensions and determinants of health - to plan public health interventions and health promotion projects - to draw a bridge from evidence to policies and politics | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Concepts of descriptive and analytical epidemiology, study designs, measures of effect, confounding and bias, screening, surveilllance, definition of health and health promotion, health dimensions and health determinants, prevention strategies, public health interventions, public health action cycle, epidemiology and prevention of infectious and chronic diseases (HIV, COVID-19, Obesity, Iodine/PH nutrition). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Handouts are provided to students in the classroom. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 636-0109-00L | Stem Cells: Biology and Therapeutic Manipulation | W | 4 credits | 3G | T. Schroeder | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Stem cells are central in tissue regeneration and repair, and hold great potential for therapy. We will discuss the role of stem cells in health and disease, and possibilities to manipulate their behavior for therapeutic application. Basic molecular and cell biology, engineering and novel technologies relevant for stem cell research and therapy will be discussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Understanding of current knowledge, and lack thereof, in stem cell biology, regenerative medicine and required technologies. Theoretical preparation for practical laboratory experimentation with stem cells. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | We will use different diseases to discuss how to potentially model, diagnose or heal them by stem cell based therapies. This will be used as a guiding framework to discuss relevant concepts and technologies in cell and molecular biology, engineering, imaging, bioinformatics, tissue engineering, that are required to manipulate stem cells for therapeutic application. Topics will include: - Embryonic and adult stem cells and their niches - Induced stem cells by directed reprogramming - Relevant basic cell biology and developmental biology - Relevant molecular biology - Cell culture systems - Cell fates and their molecular control by transcription factors and signalling pathways - Cell reprogramming - Disease modelling - Tissue engineering - Bioimaging, Bioinformatics - Single cell technologies | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 376-0225-00L | Critical Appraisal of Evidence for Exercise in Health and Disease | W | 3 credits | 2V | E. Giannouli, E. de Bruin, R. Knols | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course will discuss the mechanisms and latest evidence-based recommendations of physical activity and exercise for a series of conditions and populations. In the second part of each lecture session, published randomized controlled trials of the respective lecture`s topic will be discussed and critically appraised based on established tools. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | On completion of this course students will be able to: 1. understand the role of physical activity and sedentary behavior in the maintenance of health and the etiology, prevention and treatment of disease 2. synthesize effective physical activity and exercise interventions for the prevention and management of several diseases and populations 3. evaluate recent evidence regarding physical activity and exercise interventions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | New trends in physical activity for prevention and rehabilitation Introduction to critical appraisal tools Exercise for Cancer Rehabilitation Exercise for Musculoskeletal Rehabilitation (Focus on Osteoarthritis and Low Back Pain) Exercise in Parkinson`s disease Exercise for Rehabilitation of Metabolic Disorders (Focus on Obesity and Diabetes type 2) Exercise for age-related diseases and disorders, Part A (Focus on Frailty and Falls) Exercise for Stroke Rehabilitation Exercise in Dementia and Mild Cognitive Impairment Exercise for Children’s Rehabilitation (focus on Cerebral Palsy) Exercise for age-related diseases and disorders, Part B (Focus on Sarcopenia and Osteoporosis) Exercise in Multiple Sclerosis Exercise for Cardiovascular Rehabilitation (Focus on Heart Failure) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | • Kanosue, K., Oshima, S., Cao, Z. B., & Oka, K. (Eds.). (2015). Physical activity, exercise, sedentary behav-ior and health (No. 12152). Springer Japan. • Stensel, D. J., Hardman, A. E., & Gill, J. M. (Eds.). (2021). Physical activity and health: the evidence ex-plained. Routledge. • Xiao, J. (Ed.). (2020). Physical exercise for human health. Singapore: Springer Singapore | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Resources and Environment | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 103-0347-00L | Landscape Planning and Environmental Systems | W | 3 credits | 2V | A. Grêt-Regamey | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In the course, students learn about methods for the identification and measurement of landscape characteristics, as well as measures and policies for landscape planning. Landscape planning is put into the context of environmental systems (soil, water, air, climate, flora and fauna) and discussed with regard to socio-political questions of the future. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The aims of this course are: 1) To illustrate the concept of landscape planning, the economic relevance of landscape and nature in the context of the environmental systems (soil, water, air, climate, flora and fauna). 2) To show landscape planning as an integral information system for the coordination of different instruments by illustrating the aims, methods, instruments and their functions in landscape planning. 3) To show the importance of ecosystem services. 4) To learn basics about nature and landscape: Analysis and assessment of the complex interactions between landscape elements, effects of current and future land use (ecosystem goods and services, landscape functions). 5) To identify and measure the characteristics of landscape. 6) Learn how to use spatial data in landscape planning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | In this course, the following topics are discussed: - Definition of the concept of landscape - Relevance of landscape planning - Landscape metrics - Landscape change - Methods, instruments and aims of landscape planning (policy) - Socio-political questions of the future - Environmental systems, ecological connectivity - Ecosystem services - Urban landscape services - Practice of landscape planning - Use of GIS in landscape planning | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | No script. The documentation, consisting of presentation slides are partly handed out and are provided for download on Moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The contents of the course will be illustrated in the associated course 103-0347-01 U (Landscape Planning and Environmental Systems (GIS Exercises)) or in Project LAND within the Experimental and Computer Lab (for Environmental Engineers). A combination of courses is recommended. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 651-4057-00L | Climate History and Palaeoclimatology | W | 4 credits | 2G | H. Stoll, I. Hernández Almeida | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Climate history and paleoclimatology explores how the major features of the earth's climate system have varied in the past, and the driving forces and feedbacks for these changes. The major topics include the earth's CO2 concentration and mean temperature, the size and stability of ice sheets and sea level, the amount and distribution of precipitation, and the ocean heat transport. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The student will be able to describe the natural factors lead to variations in the earth's mean temperature, the growth and retreat of ice sheets, and variations in ocean and atmospheric circulation patterns, including feedback processes. Students will be able to interpret evidence of past climate changes from the main climate indicators or proxies recovered in geological records. Students will be able to use data from climate proxies to test if a given hypothesized mechanism for the climate change is supported or refuted. Students will be able to compare the magnitudes and rates of past changes in the carbon cycle, ice sheets, hydrological cycle, and ocean circulation, with predictions for climate changes over the next century to millennia. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course spans 5 thematic modules: 1. Cyclic variation in the earth's orbit and the rise and demise of ice sheets. Ice sheets and sea level - What do expansionist glaciers want? What is the natural range of variation in the earth's ice sheets and the consequent effect on sea level? How do cyclic variations in the earth's orbit affect the size of ice sheets under modern climate and under past warmer climates? What conditions the mean size and stability or fragility of the large polar ice caps and is their evidence that they have dynamic behavior? What rates and magnitudes of sea level change have accompanied past ice sheet variations? How stable or fragile is the ocean heat conveyor, past and present? 2. Feedbacks on climate cycles from CO2 and methane. What drives CO2 and methane variations over glacial cycles? What are the feedbacks with ocean circulation and the terrestrial biosphere? 3. Atmospheric circulation and variations in the earth's hydrological cycle - How variable are the earth's precipitation regimes? How large are the orbital scale variations in global monsoon systems? 4. Century-scale droughts and civil catastrophes. Will mean climate change El Nino frequency and intensity? What factors drive change in mid and high-latitude precipitation systems? Is there evidence that changes in water availability have played a role in the rise, demise, or dispersion of past civilizations? 5. How sensitive is Earth's long term climate to CO2 and cloud feedbacks? What regulates atmospheric CO2 over long tectonic timescales of millions to tens of millions of years? The weekly two hour lecture periods will feature lecture on these themes interspersed with short interactive tasks to apply new knowledge. Over the semester, student teams will each present in class one debate based on two scientific articles of contrasting interpretations. With flexible scheduling, students will participate in a laboratory activity to generate a new paleoclimate record from stalagmites. Student teams will be supported by an individual tutorial meeting to assist in debate preparation and another to assist in the interpretation of the lab activity data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 701-1677-00L | Quantitative Vegetation Dynamics: Models from Tree to Globe | W | 3 credits | 3G | H. Lischke, U. Hiltner, B. Rohner | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course introduces basic concepts and applications of dynamic vegetation models at various temporal and spatial scales. Different modeling approaches and underlying principles are presented and critically discussed during the lectures. In the integrated exercise parts, students work in a number of small projects with some of the introduced models to gain practical experience. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students will - be enabled to understand, assess and evaluate the fundamental properties of dynamic systems using vegetation models as case studies - obtain an overview of dynamic modelling techniques and their applications from the individual plant to the global level - understand the basic assumptions of the various model types, which dictate the applicability and limitations of the respective model - be enabled to work with such model types on their own - appreciate the methodological basis for impact assessments of future climate change and other environmental changes on ecosystems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Models of individuals - Deriving single-plant models from inventory measurements - Plant models based on 'first principles' Models at the stand scale - Simple approaches: matrix models - Competition for light and other resources as central mechanisms - Individual-based stand models: distance-dependent and distance-independent - Theoretical models Models at the landscape scale - Simple approaches: cellular automata - Dispersal and disturbances (windthrow, fire, bark beetles) as key mechanisms - Landscape models Global models - Sacrificing local detail to attain global coverage: processes and entities - Dynamic Global Vegetation Models (DGVMs) - DGVMs as components of Earth System Models | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Handouts will be available in the course and for download | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Will be indicated at the beginning of the course | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | - Ideally basic experiences in modelling and systems analysis - Basic knowledge of programming, ideally in R - Good knowledge of general ecology, ideally of vegetation dynamics and forest systems | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 701-1346-00L | Climate Change Mitigation: Carbon Dioxide Removal | W | 3 credits | 2G | N. Gruber, C. Brunner | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Future climate change can only kept within reasonable bounds when CO2 emissions are drastically reduced. In this course, we will discuss a portfolio of options involving the alteration of natural carbon sinks and carbon sequestration. The course includes introductory lectures, presentations from guest speakers from industry and the public sector, and final presentations by the students. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal of this course is to investigate, as a group, a particular set of carbon mitigation/sequestration options and to evaluate their potential, their cost, and their consequences. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | From the large number of carbon sequestration/mitigation options, a few options will be selected and then investigated in detail by the students. The results of this research will then be presented to the other students, the involved faculty, and discussed in detail by the whole group. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | None | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Will be identified based on the chosen topic. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Exam: No final exam. Pass/No-Pass is assigned based on the quality of the presentation and ensuing discussion. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0347-01L | Landscape Planning and Environmental Systems (GIS Exercises) | W | 3 credits | 2U | A. Grêt-Regamey, C. Brouillet, N. Klein, I. Nicholson Thomas | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course content of the lecture Landscape Planning and Environmental Systems (103-0347-00 V) will be illustrated in practical GIS exercises (e.g. habitat modelling, land use change, ecosystem services, connectivity). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | - Practical application of theory from the lectures - Quantitative assessment and evaluation of landscape characteristics - Learning useful applications of GIS for landscape planning - Developing landscape planning measures for practical case studies | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | - Applications of GIS in landscape planning - Landscape analysis - Landscape structural metrics - Modelling habitats and land use change - Calculating urban ecosystem services - Ecological connectivity | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | A script and presentation slides for each exercise will be provided on Moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Will be named in the lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Basic GIS skills are strongly recommended. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 701-1257-00L | European Climate Change | W | 3 credits | 2G | E. Fischer, J. Rajczak, S. C. Scherrer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The lecture provides an overview of climate change in Europe, from a physical and atmospheric science perspective. It covers the following topics: • observational datasets, observation and detection of climate change; • underlying physical processes and feedbacks; • numerical and statistical approaches; • currently available projections. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | At the end of this course, participants should: • understand the key physical processes shaping climate change in Europe; • know about the methodologies used in climate change studies, encompassing observational, numerical, as well as statistical approaches; • be familiar with relevant observational and modeling data sets; • be able to tackle simple climate change questions using available data sets. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Contents: • global context • observational data sets, analysis of climate trends and climate variability in Europe • global and regional climate modeling • statistical downscaling • key aspects of European climate change: intensification of the water cycle, Polar and Mediterranean amplification, changes in extreme events, changes in hydrology and snow cover, topographic effects • projections of European and Alpine climate change | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Slides and lecture notes will be made available at http://www.iac.ethz.ch/edu/courses/master/electives/european-climate-change.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Participants should have a background in natural sciences, and have attended introductory lectures in atmospheric sciences or meteorology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 751-5201-10L | Tropical Cropping Systems, Soils and Livelihoods (with Excursion) IMPORTANT: Students who enroll for this course are strongly recommended to verify with lecturers from other courses whether their absence of two weeks may affect their performance in the respective courses. | W | 5 credits | 10G | J. Six, K. Benabderrazik | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course guides students in analyzing and comprehending tropical agroecosystems and food systems. Students gain practical knowledge of field methods, diagnostic tools and survey methods for tropical soils and agroecosystems. An integral part of the course is the two-week field project in the Mount Kenya Region, which is co-organized with the University of Embu (Kenya) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | (1) Overview of the major land use systems in Tropical agroecosystems in several contexts Africa (2) Interdisciplinary analysis of agricultural production systems (3) Knowledge on methods to assess agroecological performance of a tropical agroecosystems (4) Hands-on training on the use of field methods, diagnostic tools and survey methods. (5) Gain practical knowledge on how to assess to climate resilience and farming systems. (6) Collaboration in international students and stakeholders | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course guides students in analyzing and comprehending tropical agroecosystems. Students of ETH Zürch will work together with the students from Embu University (Kenya) in an interdisciplinary and intercultural team. Students will focus on the Agroecological performance and climate resilience of diverse farming systems in the Mount Kenya Region. From October 28th to November 11th, The students will take part in a field course in the Mount Kenya Region. Students will then gain practical knowledge on field, meeting several stakeholders of the agricultural and food systems and conducting various assessments related to climate resilience and farming systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | We would require the students enrolled to the class to send a short cover letter (1-page max.) by September 18rd 2023, justifying your motivation to enroll to this class. A selection of 20 students will be done on the basis of the letters. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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