Search result: Catalogue data in Spring Semester 2021

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327-2225-00LMaP Distinguished Lecture Series on Soft Robotics
This course is primarily designed for MSc and doctoral students. Guests are welcome.
W1 credit2SR. Katzschmann, L. Schefer
AbstractThis course is an interdisciplinary colloquium on Soft Robotics involving different internationally renowned speakers from academia and industry giving lectures about their cutting-edge research, which highlights the state-of-the-art and frontiers in the Soft Robotics field.
ObjectiveParticipants become acquainted with the state-of-the-art and frontiers in Soft Robotics, which is a topic of global and future relevance from the field of materials and process engineering. The self-study of relevant literature and active participation in discussions following presentations by internationally renowned speakers stimulate critical thinking and allow participants to deliberately discuss challenges and opportunities with leading academics and industrial experts and to exchange ideas within an interdisciplinary community.
ContentThis course is a colloquium involving a selected mix of internationally renowned speaker from academia and industry who present their cutting-edge research in the field of Soft Robotics. The self-study of relevant pre-read literature provided in advance to each lecture serves as a basis for active participation in the critical discussions following each presentation.
Lecture notesSelected scientific pre-read literature (max. three articles per lecture) relevant for and discussed during the lectures is posted in advance on the course web page.
Prerequisites / NoticeParticipants should have a solid background in materials science and/or engineering.
376-1792-00LIntroductory Course in Neuroscience II (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: SPV0Y020

Mind the enrolment deadlines at UZH:
W2 credits2VUniversity lecturers
AbstractThis course discusses behavioral aspects in neuroscience. Modern brain imaging methods are described. Clinical issues including diseases of the nervous system are studied. Sleep research and neuroimmunology are discussed. Finally, the course deals with the basic concepts in psychiatry.
Prerequisites / NoticeFür Doktorierende des Zentrums für Neurowissenschaften Zürich.
376-1986-00LBayesian Data Analysis and Models of Behavior (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: DOEC0829

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W3 credits2SR. Polania, University lecturers
AbstractMaking sense of the data acquired via experiments is fundamental in many fields of sciences. This course is designed for students/researchers who want to gain practical experience with data analysis based on Bayesian inference. Coursework involves practical demonstrations and discussion of solutions for data analysis problems. No advanced knowledge of statistics and probability is required.
ObjectiveThe overall goal of this course it that the students are able to develop both analytic and problem-solving skills that will serve to draw reasonable inferences from observations. The first objective is to make the participants familiar with the conceptual framework of Bayesian data analysis. The second goal is to introduce the ideas of modern Bayesian data analysis, including techniques such as Markov chain Monte Carlo (MCMC) techniques, alongside the introduction of programming tools that facilitate the creation of any Bayesian inference model. Throughout the course, this will involve practical demonstrations with example datasets, homework, and discussions that should convince the participants of this course that it is possible to make inference and understand the data acquired from the experiments that they usually obtain in their own research (starting from simple linear regressions all the way up to more complex models with hierarchical structures and dependencies). After working through this course, the participants should be able to build their own inference models in order to interpret meaningfully their own data.
Prerequisites / NoticeThe very basics (or at least intuition) of programming in either Matlab or R
376-0304-00LColloquium in Translational Science (Spring Semester)W1 credit1KN. Cesarovic, A. Alimonti, C. Ewald, V. Falk, J. Goldhahn, K. Maniura, M. Ristow, R. M. Rossi, S. Schürle-Finke, G. Shivashankar, E. Vayena, V. Vogel, F. von Meyenn
AbstractCurrent topics in translational medicine presented by speakers from academia and industry.
ObjectiveGetting insight into actual areas and problems of translational medicine.
ContentTimely and concise presentations of postgraduate students, post-docs, senior scientists, professors, as well as external guests from both academics and industry will present topics of their interest related to translational medicine.
Prerequisites / NoticeNo compulsory prerequisites, but student should have basic knowledge about biomedical research.
376-0306-00LETHeart Joint Scientific Colloquium (Spring Semester)W1 credit1KN. Cesarovic, V. Falk, further lecturers
AbstractLectures, presentations and discussions on chosen topics in biologics, (bio-) materials, devices, sensors, robotics and data science and their relevance for cardiovascular medicine.
ObjectiveDeeper, mutual understanding of current medical challengesand technical solutionsin cardiovascular medicine.
ContentTimely and didacticaly stuctured presentations of postgraduate students, post-docs, senior scientists and professorson topics from Zurich Heart / ETHeart projects, followed by lectures on chosen topics of cardiovascular medicine and research given by leading international clinical scientists in the field.
Prerequisites / NoticeNo compulsory prerequisites, but students should have basic knowledge about cardiovascular system, physiology and biomedical research.
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