# Suchergebnis: Katalogdaten im Herbstsemester 2022

Biomedical Engineering Master | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Vertiefungsfächer | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Bioimaging | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Wahlfächer der Vertiefung Diese Fächer sind für die Vertiefung in Bioimaging besonders empfohlen. Bei abweichender Fächerwahl konsultieren Sie bitte den Track Adviser. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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227-0311-00L | Qubits, Electrons, Photons | W | 6 KP | 3V + 2U | T. Zambelli | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | In-depth analysis of the quantum mechanics origin of nuclear magnetic resonance (qubits, two-level systems), of LASER (quantization of the electromagnetic field, photons), and of electron transfer (from electrochemistry to photosynthesis). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | Beside electronics nanodevices, D-ITET is pushing its research in the fields of NMR (MRI), electrochemistry, bioelectronics, nano-optics, and quantum information, which are all rationalized in terms of quantum mechanics. Starting from the axioms of quantum mechanics, we will derive the fascinating theory describing spin and qubits, electron transitions and transfer, photons and LASER: quantum mechanics is different because it mocks our daily Euclidean intuition! In this way, students will work out a robust quantum mechanics (theoretical!!!) basis which will help them in their advanced studies of the following masters: EEIT (batteries), Biomedical Engineering (NMR, bioelectronics), Quantum Engineering, Micro- and Nanosystems. IMPORTANT: "qubits" from the point of view of NMR (and NOT from that of quantum computing!). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | • Lagrangian and Hamiltonian: Symmetries and Poisson Brackets • Postulates of QM: Hilbert Spaces and Operators • Heisenberg’s Matrix Mechanics: Hamiltonian and Time Evolution Operator • Density Operator • Spin: Qubits, Bloch Equations, and NMR • Entanglement • Symmetries and Corresponding Operators • Schrödinger's Wave Mechanics: Electrons in a Periodic Potential and Energy Bands • Harmonic Oscillator: Creation and Annihilation Operators • Identical Particles: Bosons and Fermions • Quantization of the Electromagnetic Field: Photons, Absorption and Emission, LASER • Electron Transfer: Marcus Theory via Born-Oppenheimer, Franck-Condon, Landau-Zener | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | No lecture notes because the proposed textbooks together with the provided supplementary material are more than exhaustive! !!!!! I am using OneNote. All lectures and exercises will be broadcast via ZOOM and correspondingly recorded (link in Moodle) !!!!! | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | • J.S. Townsend, "A Modern Approach to Quantum Mechanics", Second Edition, 2012, University Science Books • M. Le Bellac, "Quantum Physics", 2011, Cambridge University Press • (Lagrangian and Hamiltonian) L. Susskind, G. Hrabovsky, "Theoretical Minimum: What You Need to Know to Start Doing Physics", 2014, Hachette Book Group USA Supplementary material will be uploaded in Moodle. _ _ _ _ _ _ _ + (as rigorous and profound presentation of the mathematical framework) G. Dell'Antonio, "Lectures on the Mathematics of Quantum Mechanics I", 2015, Springer + (as account of those formidable years) G. Gamow, "Thirty Years that Shook Physics", 1985, Dover Publications Inc. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | The course has been intentionally conceived to be self-consistent with respect to QM for those master students not having encountered it in their track yet. Therefore, a presumably large overlapping has to be expected with a (welcome!) QM introduction course like the D-ITET "Physics II". A solid base of Analysis I & II as well as of Linear Algebra is really helpful. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kompetenzen |
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227-0421-00L | Deep Learning in Artificial and Biological Neuronal Networks | W | 4 KP | 3G | B. Grewe | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | Deep-Learning (DL) a brain-inspired weak for of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. However, DL is far from being understood and investigating learning in biological networks might serve again as a compelling inspiration to think differently about state-of-the-art ANN training methods. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | The main goal of this lecture is to provide a comprehensive overview into the learning principles neuronal networks as well as to introduce a diverse skill set (e.g. simulating a spiking neuronal network) that is required to understand learning in large, hierarchical neuronal networks. To achieve this the lectures and exercises will merge ideas, concepts and methods from machine learning and neuroscience. These will include training basic ANNs, simulating spiking neuronal networks as well as being able to read and understand the main ideas presented in today’s neuroscience papers. After this course students will be able to: - read and understand the main ideas and methods that are presented in today’s neuroscience papers - explain the basic ideas and concepts of plasticity in the mammalian brain - implement alternative ANN learning algorithms to ‘error backpropagation’ in order to train deep neuronal networks. - use a diverse set of ANN regularization methods to improve learning - simulate spiking neuronal networks that learn simple (e.g. digit classification) tasks in a supervised manner. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | Deep-learning a brain-inspired weak form of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. The origins of deep hierarchical learning can be traced back to early neuroscience research by Hubel and Wiesel in the 1960s, who first described the neuronal processing of visual inputs in the mammalian neocortex. Similar to their neocortical counterparts ANNs seem to learn by interpreting and structuring the data provided by the external world. However, while on specific tasks such as playing (video) games deep ANNs outperform humans (Minh et al, 2015, Silver et al., 2018), ANNs are still not performing on par when it comes to recognizing actions in movie data and their ability to act as generalizable problem solvers is still far behind of what the human brain seems to achieve effortlessly. Moreover, biological neuronal networks can learn far more effectively with fewer training examples, they achieve a much higher performance in recognizing complex patterns in time series data (e.g. recognizing actions in movies), they dynamically adapt to new tasks without losing performance and they achieve unmatched performance to detect and integrate out-of-domain data examples (data they have not been trained with). In other words, many of the big challenges and unknowns that have emerged in the field of deep learning over the last years are already mastered exceptionally well by biological neuronal networks in our brain. On the other hand, many facets of typical ANN design and training algorithms seem biologically implausible, such as the non-local weight updates, discrete processing of time, and scalar communication between neurons. Recent evidence suggests that learning in biological systems is the result of the complex interplay of diverse error feedback signaling processes acting at multiple scales, ranging from single synapses to entire networks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | The lecture slides will be provided as a PDF after each lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | This advanced level lecture requires some basic background in machine/deep learning. Thus, students are expected to have a basic mathematical foundation, including linear algebra, multivariate calculus, and probability. The course is not to be meant as an extended tutorial of how to train deep networks in PyTorch or Tensorflow, although these tools used. The participation in the course is subject to the following conditions: 1) The number of participants is limited to 120 students (MSc and PhDs). 2) Students must have taken the exam in Deep Learning (263-3210-00L) or have acquired equivalent knowledge. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

227-0967-00L | Computational Neuroimaging ClinicErfolgreiche Abschluss der Lehrveranstaltung "Methods & Models for fMRI Data Analysis", "Translational Neuromodeling" oder "Computational Psychiatry" | W | 3 KP | 2V | K. Stephan | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | This seminar teaches problem solving skills for computational neuroimaging, based on joint analyses of neuroimaging and behavioural data. It deals with a wide variety of real-life problems that are brought to this meeting from the neuroimaging community at Zurich, e.g. mass-univariate and multivariate analyses of fMRI/EEG data, or generative models of fMRI, EEG, or behavioural data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | 1. Consolidation of theoretical knowledge (obtained in the following courses: 'Methods & models for fMRI data analysis', 'Translational Neuromodeling', 'Computational Psychiatry') in a practical setting. 2. Acquisition of practical problem solving strategies for computational modeling of neuroimaging data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | This seminar teaches problem solving skills for computational neuroimaging, based on joint analyses of neuroimaging and behavioural data. It deals with a wide variety of real-life problems that are brought to this meeting from the neuroimaging community at Zurich, e.g. mass-univariate and multivariate analyses of fMRI/EEG data, or generative models of fMRI, EEG, or behavioural data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | The participants are expected to have successfully completed at least one of the following courses: 'Methods & models for fMRI data analysis', 'Translational Neuromodeling', 'Computational Psychiatry' | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

227-0969-00L | Methods & Models for fMRI Data Analysis | W | 6 KP | 4V | K. Stephan | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | This course teaches methods and models for fMRI data analysis, covering all aspects of statistical parametric mapping (SPM), incl. preprocessing, the general linear model, statistical inference, multiple comparison corrections, event-related designs, and Dynamic Causal Modelling (DCM), a Bayesian framework for identification of nonlinear neuronal systems from neurophysiological data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | To obtain in-depth knowledge of the theoretical foundations of SPM and DCM and of their practical application to empirical fMRI data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | This course teaches state-of-the-art methods and models for fMRI data analysis in lectures and exercises. It covers all aspects of statistical parametric mapping (SPM), incl. preprocessing, the general linear model, frequentist and Bayesian inference, multiple comparison corrections, and event-related designs, and Dynamic Causal Modelling (DCM), a Bayesian framework for identification of nonlinear neuronal systems from neurophysiological data. A particular emphasis of the course will be on methodological questions arising in the context of clinical studies in psychiatry and neurology. Practical exercises serve to consolidate the skills taught in lectures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

227-0971-00L | Computational PsychiatryPlease note that participation in this course and the practical sessions requires additional registration at: Link | W | 3 KP | 4S | K. Stephan | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | This six-day course teaches state-of-the-art methods in computational psychiatry. It covers various computational models of cognition (e.g., learning and decision-making) and brain physiology (e.g., effective connectivity) of relevance for psychiatric disorders. The course not only provides theoretical background, but also demonstrates open source software in application to concrete examples. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | This course aims at bridging the gap between mathematical modelers and clinical neuroscientists by teaching computational techniques in the context of clinical applications. The hope is that the acquisition of a joint language and tool-kit will enable more effective communication and joint translational research between fields that are usually worlds apart. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | This six-day course teaches state-of-the-art methods in computational psychiatry. It covers various computational models of cognition (e.g., learning and decision-making) and brain physiology (e.g., effective connectivity) of relevance for psychiatric disorders. The course not only provides theoretical background, but also demonstrates open source software in application to concrete examples. Furthermore, practical exercises provide in-depth exposure to different software packages. Please see Link for details. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

227-1033-00L | Neuromorphic Engineering I Registration in this class requires the permission of the instructors. Class size will be limited to available lab spots. Preference is given to students that require this class as part of their major. Information for UZH students: Enrolment to this course unit only possible at ETH. No enrolment to module INI404 at UZH. Please mind the ETH enrolment deadlines for UZH students: Link | W | 6 KP | 2V + 3U | T. Delbrück, G. Indiveri, S.‑C. Liu | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | This course covers analog circuits with emphasis on neuromorphic engineering: MOS transistors in CMOS technology, static circuits, dynamic circuits, systems (silicon neuron, silicon retina, silicon cochlea) with an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | Understanding of the characteristics of neuromorphic circuit elements. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | Neuromorphic circuits are inspired by the organizing principles of biological neural circuits. Their computational primitives are based on physics of semiconductor devices. Neuromorphic architectures often rely on collective computation in parallel networks. Adaptation, learning and memory are implemented locally within the individual computational elements. Transistors are often operated in weak inversion (below threshold), where they exhibit exponential I-V characteristics and low currents. These properties lead to the feasibility of high-density, low-power implementations of functions that are computationally intensive in other paradigms. Application domains of neuromorphic circuits include silicon retinas and cochleas for machine vision and audition, real-time emulations of networks of biological neurons, and the development of autonomous robotic systems. This course covers devices in CMOS technology (MOS transistor below and above threshold, floating-gate MOS transistor, phototransducers), static circuits (differential pair, current mirror, transconductance amplifiers, etc.), dynamic circuits (linear and nonlinear filters, adaptive circuits), systems (silicon neuron, silicon retina and cochlea) and an introduction to multi-chip systems that communicate events analogous to spikes. The lectures are accompanied by weekly laboratory sessions on the characterization of neuromorphic circuits, from elementary devices to systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | S.-C. Liu et al.: Analog VLSI Circuits and Principles; various publications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | Particular: The course is highly recommended for those who intend to take the spring semester course 'Neuromorphic Engineering II', that teaches the conception, simulation, and physical layout of such circuits with chip design tools. Prerequisites: Background in basics of semiconductor physics helpful, but not required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

227-1037-00L | Introduction to Neuroinformatics | W | 6 KP | 2V + 1U + 1A | V. Mante, M. Cook, B. Grewe, G. Indiveri, D. Kiper, W. von der Behrens | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | The course provides an introduction to the functional properties of neurons. Particularly the description of membrane electrical properties (action potentials, channels), neuronal anatomy, synaptic structures, and neuronal networks. Simple models of computation, learning, and behavior will be explained. Some artificial systems (robot, chip) are presented. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | Understanding computation by neurons and neuronal circuits is one of the great challenges of science. Many different disciplines can contribute their tools and concepts to solving mysteries of neural computation. The goal of this introductory course is to introduce the monocultures of physics, maths, computer science, engineering, biology, psychology, and even philosophy and history, to discover the enchantments and challenges that we all face in taking on this major 21st century problem and how each discipline can contribute to discovering solutions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | This course considers the structure and function of biological neural networks at different levels. The function of neural networks lies fundamentally in their wiring and in the electro-chemical properties of nerve cell membranes. Thus, the biological structure of the nerve cell needs to be understood if biologically-realistic models are to be constructed. These simpler models are used to estimate the electrical current flow through dendritic cables and explore how a more complex geometry of neurons influences this current flow. The active properties of nerves are studied to understand both sensory transduction and the generation and transmission of nerve impulses along axons. The concept of local neuronal circuits arises in the context of the rules governing the formation of nerve connections and topographic projections within the nervous system. Communication between neurons in the network can be thought of as information flow across synapses, which can be modified by experience. We need an understanding of the action of inhibitory and excitatory neurotransmitters and neuromodulators, so that the dynamics and logic of synapses can be interpreted. Finally, simple neural architectures of feedforward and recurrent networks are discussed in the context of co-ordination, control, and integration of sensory and motor information. Connections to computer science and artificial intelligence are discussed, but the main focus of the course is on establishing the biological basis of computations in neurons. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

227-2037-00L | Physical Modelling and Simulation | W | 6 KP | 4G | J. Smajic | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | This module consists of (a) an introduction to fundamental equations of electromagnetics, mechanics and heat transfer, (b) a detailed overview of numerical methods for field simulations, and (c) practical examples solved in form of small projects. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | Basic knowledge of the fundamental equations and effects of electromagnetics, mechanics, and heat transfer. Knowledge of the main concepts of numerical methods for physical modelling and simulation. Ability (a) to develop own simple field simulation programs, (b) to select an appropriate field solver for a given problem, (c) to perform field simulations, (d) to evaluate the obtained results, and (e) to interactively improve the models until sufficiently accurate results are obtained. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | The module begins with an introduction to the fundamental equations and effects of electromagnetics, mechanics, and heat transfer. After the introduction follows a detailed overview of the available numerical methods for solving electromagnetic, thermal and mechanical boundary value problems. This part of the course contains a general introduction into numerical methods, differential and integral forms, linear equation systems, Finite Difference Method (FDM), Boundary Element Method (BEM), Method of Moments (MoM), Multiple Multipole Program (MMP) and Finite Element Method (FEM). The theoretical part of the course finishes with a presentation of multiphysics simulations through several practical examples of HF-engineering such as coupled electromagnetic-mechanical and electromagnetic-thermal analysis of MEMS. In the second part of the course the students will work in small groups on practical simulation problems. For solving practical problems the students can develop and use own simulation programs or chose an appropriate commercial field solver for their specific problem. This practical simulation work of the students is supervised by the lecturers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

151-0605-00L | Nanosystems | W | 4 KP | 4G | A. Stemmer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | From atoms to molecules to condensed matter: characteristic properties of simple nanosystems and how they evolve when moving towards complex ensembles. Intermolecular forces, their macroscopic manifestations, and ways to control such interactions. Self-assembly and directed assembly of 2D and 3D structures. Special emphasis on the emerging field of molecular electronic devices. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | Familiarize students with basic science and engineering principles governing the nano domain. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | The course addresses basic science and engineering principles ruling the nano domain. We particularly work out the links between topics that are traditionally taught separately. Familiarity with basic concepts of quantum mechanics is expected. Special emphasis is placed on the emerging field of molecular electronic devices, their working principles, applications, and how they may be assembled. Topics are treated in 2 blocks: (I) From Quantum to Continuum From atoms to molecules to condensed matter: characteristic properties of simple nanosystems and how they evolve when moving towards complex ensembles. (II) Interaction Forces on the Micro and Nano Scale Intermolecular forces, their macroscopic manifestations, and ways to control such interactions. Self-assembly and directed assembly of 2D and 3D structures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | - Kuhn, Hans; Försterling, H.D.: Principles of Physical Chemistry. Understanding Molecules, Molecular Assemblies, Supramolecular Machines. 1999, Wiley, ISBN: 0-471-95902-2 - Chen, Gang: Nanoscale Energy Transport and Conversion. 2005, Oxford University Press, ISBN: 978-0-19-515942-4 - Ouisse, Thierry: Electron Transport in Nanostructures and Mesoscopic Devices. 2008, Wiley, ISBN: 978-1-84821-050-9 - Wolf, Edward L.: Nanophysics and Nanotechnology. 2004, Wiley-VCH, ISBN: 3-527-40407-4 - Israelachvili, Jacob N.: Intermolecular and Surface Forces. 2nd ed., 1992, Academic Press,ISBN: 0-12-375181-0 - Evans, D.F.; Wennerstrom, H.: The Colloidal Domain. Where Physics, Chemistry, Biology, and Technology Meet. Advances in Interfacial Engineering Series. 2nd ed., 1999, Wiley, ISBN: 0-471-24247-0 - Hunter, Robert J.: Foundations of Colloid Science. 2nd ed., 2001, Oxford, ISBN: 0-19-850502-7 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | Course format: Lectures and Mini-Review presentations: Thursday 10-13 Homework: Mini-Review (compulsory continuous performance assessment) Each student selects a paper (list distributed in class) and expands the topic into a Mini-Review that illuminates the particular field beyond the immediate results reported in the paper. Each Mini-Review will be presented both orally and as a written paper. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

252-0543-01L | Computer Graphics | W | 8 KP | 3V + 2U + 2A | M. Gross, M. Papas | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | This course covers some of the fundamental concepts of computer graphics generation of photorealistic images from digital representations of 3D scenes and image-based methods for recovering digital scene representations from captured images. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | At the end of the course the students will be able to build a rendering system. The students will study the basic principles of rendering and image synthesis. In addition, the course is intended to stimulate the students' curiosity to explore the field of computer graphics in subsequent courses or on their own. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | This course covers fundamental concepts of modern computer graphics. Students will learn about 3D object representations and the details of how to generate photorealistic images from digital representations of 3D scenes. Starting with an introduction to 3D shape modeling, geometry representation and texture mapping, we will move on to the physics of light transport, acceleration structures, appearance modeling and Monte Carlo integration. We will apply these principles for computing light transport of direct and global illumination due to surfaces and participating media. We will end with an overview of modern image-based capture and image synthesis methods, covering topics such as geometry and material capture, light-fields and depth-image based rendering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | Books: High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting Multiple view geometry in computer vision Physically Based Rendering: From Theory to Implementation | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | Prerequisites: Fundamentals of calculus and linear algebra, basic concepts of algorithms and data structures, programming skills in C++, Visual Computing course recommended. The programming assignments will be in C++. This will not be taught in the class. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

402-0674-00L | Physics in Medical Research: From Atoms to Cells | W | 6 KP | 2V + 1U | B. K. R. Müller | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | Scanning probe and diffraction techniques allow studying activated atomic processes during early stages of epitaxial growth. For quantitative description, rate equation analysis, mean-field nucleation and scaling theories are applied on systems ranging from simple metallic to complex organic materials. The knowledge is expanded to optical and electronic properties as well as to proteins and cells. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | The lecture series is motivated by an overview covering the skin of the crystals, roughness analysis, contact angle measurements, protein absorption/activity and monocyte behaviour. As the first step, real structures on clean surfaces including surface reconstructions and surface relaxations, defects in crystals are presented, before the preparation of clean metallic, semiconducting, oxidic and organic surfaces are introduced. The atomic processes on surfaces are activated by the increase of the substrate temperature. They can be studied using scanning tunneling microscopy (STM) and atomic force microscopy (AFM). The combination with molecular beam epitaxy (MBE) allows determining the sizes of the critical nuclei and the other activated processes in a hierarchical fashion. The evolution of the surface morphology is characterized by the density and size distribution of the nanostructures that could be quantified by means of the rate equation analysis, the mean-field nucleation theory, as well as the scaling theory. The surface morphology is further characterized by defects and nanostructure's shapes, which are based on the strain relieving mechanisms and kinetic growth processes. High-resolution electron diffraction is complementary to scanning probe techniques and provides exact mean values. Some phenomena are quantitatively described by the kinematic theory and perfectly understood by means of the Ewald construction. Other phenomena need to be described by the more complex dynamical theory. Electron diffraction is not only associated with elastic scattering but also inelastic excitation mechanisms that reflect the electronic structure of the surfaces studied. Low-energy electrons lead to phonon and high-energy electrons to plasmon excitations. Both effects are perfectly described by dipole and impact scattering. Thin-films of rather complex organic materials are often quantitatively characterized by photons with a broad range of wavelengths from ultra-violet to infra-red light. Asymmetries and preferential orientations of the (anisotropic) molecules are verified using the optical dichroism and second harmonic generation measurements. Recently, ellipsometry has been introduced to on-line monitor film thickness, and roughness with sub-nanometer precision. These characterisation techniques are vital for optimising the preparation of medical implants. Cell-surface interactions are related to the cell adhesion and the contractile cellular forces. Physical means have been developed to quantify these interactions. Other physical techniques are introduced in cell biology, namely to count and sort cells, to study cell proliferation and metabolism and to determine the relation between cell morphology and function. X rays are more and more often used to characterise the human tissues down to the nanometer level. The combination of highly intense beams only some micrometers in diameter with scanning enables spatially resolved measurements and the determination of tissue's anisotropies of biopsies. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

465-0953-00L | Biostatistics | W | 4 KP | 2V + 1U | B. Sick | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | Der Kurs behandelt einfache quantitative und graphische als auch komplexere Methoden der Biostatistik. Inhalt: Deskriptive Statistik, Prüfung von Hypothesen, Konfidenzintervalle, Korrelation, einfache und multiple lineare Regression, Klassifikation und Prognose, Diagnostische Tests, Bestimmung der Zuverlässigkeit von Messungen, Kausalität versus Korrelation | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | - Kennen der gängigsten Methoden der Biostatistik - einfache Analysen können mit R durchgeführt werden | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

227-0976-00L | Computational Psychiatry & Computational Psychosomatics Findet dieses Semester nicht statt. Number of participants limited to 24. Information for UZH students: Enrolment to this course unit only possible at ETH Zurich. No enrolment to module BMT20002. Please mind the ETH enrolment deadlines for UZH students: Link | W | 2 KP | 4S | K. Stephan | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | This seminar deals with the development of clinically relevant computational tools and/or their application to psychiatry and psychosomatics. It is complementary to the annual Computational Psychiatry Course and serves to build bridges between computational scientists and clinicians. It is designed to foster in-depth exchange, with ample time for discussion. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | Understanding strengths and weaknesses of current trends in the development of clinically relevant computational tools and their application to problems in psychiatry and psychosomatics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | This seminar deals with the development of computational tools (e.g. generative models, machine learning) and/or their application to psychiatry and psychosomatics. The seminar includes (i) presentations by computational scientists and clinicians, (ii) group discussion with focus on methodology and clinical utility, (iii) self-study based on literature provided by presenters. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | Literature for additional self-study of the topics presented in this seminar will be provided by the presenters and will be available online at Link | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | Participants are expected to be familiar with general principles of statistics (including Bayesian statistics) and have successfully completed the course “Computational Psychiatry” (Course number 227-0971-00L). |

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