. "Physics"@en . . "Electrical engineering"@en . . "Remote Sensing"@en . . "Space System engineering"@en . . "Satellite Engineering"@en . . "Computer Science"@en . . "Astronomy"@en . . "English"@en . . "The space environment"@en . . "5.00" . "Students are given an overview of the space environment, including the vacuum environment; the neutral environment; the plasma environment; the radiation environment; and the micrometeoroid/orbital debris environment. The distinguishing characteristics of different satellite orbits and their uses for a range of space-based applications (e.g. Earth observation and astrophysics, solar system transfers) are developed. The essentials of spacecraft subsystems engineering, including rocket propulsion, are discussed.\n\nLearning Outcomes:\nOn completion of this course, the student should be able to:\n- construct suitable orbits for various satellite and spacecraft applications;\n- compare and contrast space environments of different orbits;\n- identify the main effects of the space environment on satellites, and outline mitigation strategies;\n- understand and describe the critical elements of spacecraft subsystems engineering;\n- apply fundamental physical principles to rocket propulsion." . . "Presential"@en . "TRUE" . . "Applications of space science"@en . . "5.00" . "This module presents an overview of applications of space science and technology, including: astronomy, cosmology and planetary science; Earth observation and remote sensing; satellite services such as telecommunications and satellite navigation.\n\nLearning Outcomes:\nOn completion of this course, the student should be able to:\n- identify current open scientific questions in the fields of astronomy, cosmology and Earth science;\n- relate these scientific drivers to the necessary space technology;\n- summarise the data handling and communications systems needed for spacecraft operation;\n- describe the operation of satellite navigation systems, such as GPS and Galileo." . . "Presential"@en . "TRUE" . . "Space sector professional skills"@en . . "5.00" . "This module gives students the opportunity to develop professional skills relevant to the space sector. These include space project management, developing requirements, proposal writing in response to Invitations to Tender, CV preparation and other relevant transferable skills such as team-working.\n\nOne-to-one access to space sector professionals, via seminars and workshops, provides students with a unique opportunity to network and gain insights that will support them in planning their own preferred career trajectory.\n\nThe module provides a structured environment within which students develop their CV and cover letter, in consultation with the internship manager and other support services. Students are supported to seek out opportunities for their 3 month space sector internships in a timely and professional manner.\n\nWeekly seminars provide students with a breadth of knowledge of the current state of the art across the space sector from experts in the field.\n\nLearning Outcomes:\nOn completion of this module, students should be able to:\n- reflect on career aspirations and critically assess their own strengths and weaknesses;\n- assess key areas of opportunity within the space sector through seminars and networking;\n- synthesise their expertise and career goals into a compelling and professional CV and cover letter;\n- research suitable internship opportunities and craft a compelling internship application;\n- identify key team-working skills and the attributes of successful teams;\n- analyse the role of the project manager in the space sector and the need for technical project management skills;\n- outline the space project life-cycle from Phases A to F and link to relevant European standards such as ECSS;\n- analyse and respond to an 'Invitation to Tender' by the European Space Agency, through practice in planning, structuring and writing a proposal according to best practice of prime contractors;\n- compose requirements using established industry and agency practice.\n\nIndicative Module Content:\nThe content of this module is as follows:\n- 12 lectures and space sector seminars (Trimester 1)\n- 4 workshops (Trimester 2). The workshops address (i) Requirements; (ii) Teams; (iii) Space Project Management; (iv) Proposal Writing in Response to an ESA Invitation to Tender, with a feedback session." . . "Blended"@en . "TRUE" . . "Satellite subsystems"@en . . "10.00" . "One problem inherent with space projects is the length of time from concept through launch and operations.\n\nThe aim of this course is to bring each student team (3-4 students) through the complete satellite system development process in one trimester. The aim is to make these small satellites (which we call ‘TupperSats’) as capable as possible and to (i) develop a payload compatible with platform, budget and mass constraints; (ii) launch these satellites by weather balloon, or other suitable launch vehicle; (iii) operate the satellites and telemeter data and (iv) recover the satellite.\n\nStudents are introduced to project management, project phases, systems engineering, collaborative tools (e.g. GitHub) and documentation.\n\nLearning Outcomes:\nOn completion of this course, student should be able to:\n\n• Implement a simplified space system development process, including documentation\n• Write code in Python to run the instruments on the satellite and communicate data to the ground station\n• Work effectively as part of a multi-disciplinary team and stick to a schedule\n• Prototype different instrument concepts\n• Assemble, integrate and test the complete satellite\n• Launch the satellite using a suitable vehicle (weather balloon…)\n• Operate the satellite using a ground station to collect data\n• Recover the satellite using portable tracking equipment and analyse data" . . "Presential"@en . "TRUE" . . "Space detector laboratory"@en . . "10.00" . "Space missions use a wide variety of detectors and sensors to answer questions in space science & astronomy. In this module, students will use detectors of various wavelengths to learn how they work and why they are used.\n\nPractical laboratories will include hands-on experience in characterising and calibrating gamma-ray detectors in the lab, and simulating detector performance in the space environment. Students will use Python to build data analysis pipelines to assess the performance of detectors including scintillating crystals and cryogenically cooled germanium detectors.\n\nStudents will also work with a custom nanosatellite simulator, EduCube, to understand how experiments are integrated into a space mission.\n\nThis module is continuously assessed based on (individual and group) written assignments and lab work.\n\nThis module is a prerequisite for Space Mission Design (PHYC40880)\n\nLearning Outcomes:\nOn completion of this course, the student should be able to:\n- describe the interactions of photons of various wavelengths with different detector materials\n- differentiate between the requirements of detectors in different wavelength bands\n\nThe student should be able to:\n- describe and explain the operation of gamma-ray detectors\n- build data analysis pipelines to calibrate and characterise the performance of a gamma-ray detector\n- assess the suitability of different gamma-ray detectors for space applications\n- apply basic radar and signal processing principles to problems in synthetic aperture radar imaging\n\nThe student should also be able to:\n- explain how and why nanosatellites are used in astronomy & space science\n- describe and explain the purpose and basic operation of subsystems in scientific nanosatellites" . . "Presential"@en . "TRUE" . . "Space mission design"@en . . "10.00" . "This is a design study for an astronomical spacecraft which takes usually place at La Laguna University (ULL), Tenerife, Spain. It is likely that the trip will take place remotely in 2020/2021. The focus of this module is to carry out a design study for an astronomical spacecraft mission with instrumentation for the detection of gamma-rays from astrophysical sources.\n\nThe students make use of lectures, tutorials, computer and library facilities to assemble a review of the state of knowledge for such a proposed instrumentation challenge. They then work in parallel in teams of students. The aim is to produce a well thought-out spacecraft design for a specific gamma-ray astrophysics goal, after a concentrated period of intensive work.\n\nA series of approximately 12 lectures will be given in UCD prior to the trip, in order to provide students with the necessary basic technical skills required for undertaking the study. Students will be required to perform an extensive review of current gamma-ray missions and to review the literature in relation to science goals in high energy astrophysics.\n\nAdvisors will be available to assist the student teams with various aspects of their task. For example, they would provide support in such areas as: computation, detector design, astrophysics, mission planning and the environment of space. Each team will be directly supervised by a member of staff from either UCD, Southampton or ULL. Depending on numbers, this field trip may not run in every academic year. This trip takes place in late March/early April.\n\nLearning Outcomes:\nOn completion of this module students will be able to:\n- work in small teams with each member having a specific responsibility, and know how to interact positively with other members of a close team,\n- perform a detailed of the relevant literature\n- devise a solution to a complicated problem in a relatively short period of time,\n- work closely with people from a different country and background,\n- write software pipelines in a suitable language (e.g. Python) for the simulation of spacecraft instrument performance and sensitivity\n- develop presentation and research skills\n- prepare a mission feasibility study\n- present scientific results comprehensively and fluently, orally and in writing" . . "Presential"@en . "TRUE" . . "Data science in python (md)"@en . . "5.00" . "The key objectives of this module are\n1) to provide students with an initial crash course in Python programming;\n2) to familiarise students with a range of key topics in the emerging field of Data Science through the medium of Python.\nStudents will start by exploring methods for collecting, storing, filtering, and analysing datasets. From there, the module will introduce core concepts from numerical computing, statistics, and machine learning, and demonstrate how these can be applied in practice using popular open source packages and tools. Additional topics that will be covered include data visualisation and working with textual data. This module has a strong practical programming focus and students will be expected to complete two detailed coursework assignments, each involving implementing a Python solution to a data analytics task. COMP47670 requires a reasonable level of mathematical ability, and students should have prior programming experience (but not necessarily in Python).\nThis is a Mixed Delivery module with online lectures and face to face practicals/tutorials\n\nLearning Outcomes:\nOn completion of this module, students will be able to:\n1) Program competently using Python and be familiar with a range of Python packages for data science;\n2) Collect, pre-process and filter datasets;\n3) Apply and evaluate machine learning algorithms in Python;\n4) Visualise and interpret the results of data analysis procedures.." . . "Blended"@en . "FALSE" . . "Machine learning with python"@en . . "5.00" . "The objective of this module is to familiarise students with the fundamental theoretical concepts in machine learning, as well as to instruct students in the practical aspects of applying machine learning algorithms. Key techniques in supervised machine learning will be covered, such as classification using decision trees and nearest neighbour algorithms, and regression analysis. A particular emphasis will be placed on the evaluation of the performance of these algorithms. In unsupervised machine learning, a number of popular clustering algorithms will be presented in detail. Further topics covered include ensemble learning, dimension reduction, and model selection. This module requires strong mathematical ability, as some of the algorithms require some understanding of linear algebra and statistical concepts. Exercises and assignments will use the machine learning libraries in Python.\n\nLearning Outcomes:\nOn completion of this module, students will be able to:\n1) Distinguish between the different categories of machine learning algorithms;\n2) Identify a suitable machine learning algorithm for a given application or task;\n3) Run and evaluate the performance of a range of algorithms on real datasets using Python libraries." . . "Presential"@en . "FALSE" . . "Machine learning with python (online)"@en . . "5.00" . "The objective of this module is to familiarise students with the fundamental theoretical concepts in machine learning, as well as to instruct students in the practical aspects of applying machine learning algorithms. Key techniques in supervised machine learning will be covered, such as classification using decision trees and nearest neighbour algorithms, and regression analysis. A particular emphasis will be placed on the evaluation of the performance of these algorithms. In unsupervised machine learning, a number of popular clustering algorithms will be presented in detail. Further topics covered include ensemble learning, dimension reduction and model selection. This module requires strong mathematical ability, as some of the algorithms require some understanding of linear algebra and statistical concepts. Exercises and assignments will use the machine learning libraries in Python.\n\nLearning Outcomes:\nOn completion of this module, students will be able to:\n1) Distinguish between the different categories of machine learning algorithms;\n2) Identify a suitable machine learning algorithm for a given application or task;\n3) Run and evaluate the performance of a range of algorithms on real datasets using Python libraries." . . "Online"@en . "FALSE" . . "Planetary geomorphology"@en . . "5.00" . "Our solar system is endowed with a fascinating family of planets and planetary bodies. Some are giant gas planets, like Jupiter, but most are smaller rocky or icy bodies. This group of smaller planets and satellites includes Earth. Intriguingly, the other bodies in this group share many geomorphological characteristics with Earth, pointing to many shared environmental processes: all have a history of planetary bombardment and cratering; some have atmospheres and show evidence of wind-sculpting, e.g.Venus, Mars and Titan; volcanism has been, or is currently, an important surface-producing agent on at least three, Venus, Mars and Jupiter’s satellite Io; Venus is the near-twin of Earth in size and has a dense atmosphere but its evolution has been very different from Earth’s, with crushing surface pressure, searing temperatures and aggressive atmospheric chemistry; several large satellites are shrouded in a mobile crust of ice overlying a global liquid ocean, e.g. Europa, Ganymede and Enceladus; the giant satellite Titan has a dense atmosphere that is chemically very similar to the Earth’s first atmosphere and it shows abundant evidence of a ‘hydrological’ cycle (although not involving water), including the presence of rivers and lakes; Mars, tantalizingly similar to Earth, is distinctly not an identical twin but it is relatively nearby and has been visited by many orbiters and landers that have shown it to be, or have been, very Earth-like at certain places and/or times or in specific process-domains. Given the close, but tantalizingly different, planetary evolution of Earth and Mars, the wealth of data available and the potential Mars offers for learning and research, including learning more about our planet, it will be the primary focus of this module, with an emphasis on the processes and landforms associated with water in all its phases (i.e. ice, liquid and vapour).\n\nCurrently, the best way to understand the geomorphology of another planet, and hence the environmental processes operating at the surface of that planet, is to find analogous landform assemblages here on Earth and to study as many of their genetic factors as possible. Many landforms and geomorphological assemblages on Mars are analogous to morphologies on Earth that formed in volcanic, aeolian, fluvial, lacustrine, marine, periglacial, glaciofluvial and glacial process environments. These include: volcanoes and lava; sand dunes and yardangs; rivers, gullies and river networks; lake basins and shorelines; extensive marine basins, seabeds and shorelines; rock glaciers and glaciers, patterned ground (polygons), sorted periglacial landforms, thermokarst and pingos. The discovery of these landforms on Mars, in high-resolution images of the surface, has led to the conclusion that volcanism, wind, liquid water and ice have collaborated to produce a very Earth-like planetary surface. However, the geomorphology of Mars is showing evidence of one or more recent major changes in Martian climate, possibly including brief periods when water recently became morphologically effective. The likely cause for such a change is orbitally-driven variability in the axial obliquity of Mars. The same process is a major factor behind the repeated cycles of glaciation experienced by Earth over the last 2 Ma. If this can be confirmed, it would have major implications for our understanding of climate and water on Mars and would tell us more about the processes of environmental change on Earth, including the feedbacks between climate forcing, global warming, cryospheric stability and the hydrological cycle. Many tailored field campaigns are active on Earth, with research agendas that are Mars-specific and targeted, for example, at parameterization of key morphologies as proxies for those key processes, i.e. climate change, cryospheric stability and the cycling of water from sources to sinks. Insights from these analogue studies should provide a better understanding of the relationships between landforms, surface materials (including chemistries) and the surface processes of both Mars and Earth. For that reason, this analogue approach to planetary geomorphology will be the focus of this module, both conceptually and methodologically.\n\nLearning Outcomes:\nStudents will be introduced to the major areas of research in planetary geomorphology, the datasets available and the methodologies of planetary geomorphology, all with a special focus on the geomorphology of Mars. From working in and studying for this module students should gain an understanding of the diversity of planetary geomorphology and planetary evolution in our solar system." . . "Blended"@en . "FALSE" . . "Remote sensing"@en . . "10.00" . "Remote Sensing is a core focus of contemporary GIS application, both in research and professional / business contexts. The purpose of this course is to provide adequate knowledge pertaining to concepts, principles and utility of Remote Sensing technology and to prepare students to apply this technology to their discipline of interest. It will also provide a sound understanding of principles and applications of remotely sensed digital image processing. The specification and use of digital imagery for investigating Earth resources and environmental applications will be discussed. Digital image processing of aerial/space borne sensors including radiometric and geometric correction, image enhancement and interpretation, mosaicking, segmentation a swell as classification techniques and its integration with GIS will be covered.\n\nLearning Outcomes:\nOn completion of the module you will have gained the following skills:\n\n- Understanding of theoretical remote sensing considerations and technical information pertaining to a range of sensor platforms.\n- Ability to use the complete range of remote sensing tools for a broad range of operational and application tasks.\n- Ability to efficiently and accurately correct and interpret remotely sensed digital imagery.\n- Understanding on the use of statistics pertainning to radiometric/geometric correction and classification as well as segmentation techniques.\n- Knowledge and application of image enhancement techniques.\n- Knowledge to use the electromagnetic spectrum to generate a variety of image products.\n- Ability to discuss the interaction of remotely sensed data in a GIS and vice versa at a philosophical and practical level." . . "Blended"@en . "FALSE" . . "Stellar astrophysics & astronomical techniques"@en . . "5.00" . "The first part of this module is concerned with our understanding of the births, lives and deaths of stars. The starting point is the observational study and classification of stars, arriving at the Hertzsprung-Russell and Mass-Luminosity diagrams. The physics of stars, including the mechanisms by which stars support themselves against gravitational collapse, and how they derive power from nuclear processes and generate elements heavier than Helium, is then examined. The final section of the course is dedicated to astronomical instrumentation where the design of telescopes used in astronomy to detect electromagnetic radiation from radio waves to gamma rays is explored. The module draws on ideas and laws from many different areas of physics and so a reasonable background in physics is expected for students to undertake this course.\n\nLearning Outcomes:\nOn completion of this module students should be able to:\n(1) describe the techniques and results of observations of stars\n(2) derive/calculate information about stars' physical properties from the measurement data\n(3) apply the laws of physics to understand the properties and evolution of stars, and apply models to determine parameters such as central pressure, central temperature, lifetime etc.\n(4) describe the processes of stellar nucleosynthesis\n(5) describe the compact objects that form at the end of stars' lives, including White Dwarf stars, Neutron stars and Pulsars.\n(6) discuss the detection methods and techniques used by astronomical telescopes for operation in different parts of the electromagnetic spectrum, and perform basic calculations of telescope performance and sensitivity\n\nIndicative Module Content:\nRough outline of the course\n\n- Introduction: stellar properties (distances, magnitude, luminosities, etc); The HR diagram; ...\n\n- Stellar structure\n\n- Stellar evolution\n\n- Astronomical techniques: Earth atmosphere; Fundamental concepts; Telescopes" . . "Presential"@en . "FALSE" . . "Galaxies, observational cosmology & the interstellar medium"@en . . "5.00" . "The module addresses how galaxies form, are classified and how they cluster in space. The Milky Way Galaxy will be discussed in detail. A distinction will be drawn between Normal and Active galaxies. The powering of Active galaxies by supermassive black holes will be discussed in the context of the luminosities of galaxies. Galactic rotation curves will be discussed from the perspective of the estimation of galaxy masses. Such measurements suggest the underlying presence of dark matter which is not directly detectable.A description of modern cosmology will be given, based on experimental data from contemporary observations. Evidence for the accelerated expansion of the universe will be presented which points to the presence of a dark energy component of the cosmos that contributes a weak repulsive force throughout spacetime, underpinning the accelerated expansion.Additionally the interstellar medium will be introduced in particular with respect to describing the structure, dynamics and evolution of galaxies.\n\nLearning Outcomes:\nOn completion of this module students should be able to describe:(1) The Milky Way Galaxy.(2) Galaxy evolution and the classification of galaxies.(3) The interstellar medium(4) Cosmological Models." . . "Presential"@en . "FALSE" . . "Theoretical astrophysics"@en . . "5.00" . "Theoretical astrophysics is concerned with the application of the fundamental principles and equations of physics to solve astrophysical problems and to explain astronomical observations. This module covers the theory of radiative processes, astrophysical gas dynamics and an introduction to astrophysical plasmas. Radiative processes includes the equation of radiative transfer and the fundamental theory of radiation fields. The module will then specifically examine bremsstrahlung, synchrotron, Compton, inverse-Compton and line emission mechanisms. Gas dynamics includes basic principles, hydrostatics, waves, shock fronts, accretion, outflows, instabilities and an introduction to plasma physics. These topics will be applied to an extensive range of astronomical problems and observations; including the hot big bang model, aspects of stellar structure and evolution, compact objects, the interstellar medium and high energy astrophysics.\n\nLearning Outcomes:\nOn successful completion of this module a student will (1) understand the principles of astrophysical radiative processes and gas dynamics, (2) understand how fundamental areas of physics combine in different astrophysical settings, (3) solve problems in radiative processes and gas dydnamics and (4) explain select astronomical observations using the tools of theoretical astrophysics." . . "Presential"@en . "FALSE" . . "Physics demonstrating\\tutoring"@en . . "5.00" . "This module will introduce the graduate student to the theory & practice of small class tutoring and demonstrating in physics. The module will support you in your development as an instructor, and is based heavily on class contact and encourages reflective practice, i.e. taking time to think about what you do in a small class environment.\n\nLearning Outcomes:\nThoroughly understand the content of the module(s) in which you are instructing.\nKnow how to communicate, deliver feedback and maintain discipline.\nBe aware of the relevant safety requirements in laboratory/classroom." . . "Blended"@en . "FALSE" . . "Physics data analysis (python)"@en . . "5.00" . "The aim is to provide students with a strong grounding in the analysis of experimental Physics data in the Python programming language. The contents will cover the basics of statistics, error analysis and propagation of errors, curve fitting and parameter estimation, chi-squared tests for goodness of fit, Monte Carlo simulations and maximum likelihood methods. Python topics will be intertwined with data analysis topics to build Python skills at the same time. Students will learn from doing examples themselves in-class in an Active Learning Room environment as well as assignments. The error analysis section of the course will pay close attention to the Guide to the expression of Uncertainty in Measurement (G.U.M.) reference document adopted by many scientific organisations and industries.\n\nLearning Outcomes:\nHave an understanding of experimental measurement and uncertainties, including statistical and systematic errors, and to use appropriate precision when quoting uncertainties.\n\nUnderstand the fundamental statistical distributions that apply to physical measurements.\n\nBe able to characterise data through parameters such as the mean, standard deviation, covariance, weighted mean and uncertainties on the weighted mean.\n\nBe able to propagate errors on measurements through functions of those measurements, both analytically and numerically.\n\nBe able to fit a function to a set of experimental data to derive best-fit parameters including the uncertainties on the parameters and to use the best-fit covariance matrix to calculate confidence intervals.\n\nBe able to apply a chi-squared test to assess goodness of fit and f-test to assess whether extra parameters for nested functions significantly improve the fit.\n\nBe able to apply Kolmogorov–Smirnov test and chi-square tests to compare two distributions.\n\nHave an understanding of and be able to apply the Permutation test and Bootstrap/Jackknife tests.\n\nBe apply to apply the Method of Maximum Likelihood, including the Likelihood Ratio Test, for parameter estimation and significance estimation.\n\nBe able to do all of the above in Python using appropriate libraries." . . "Presential"@en . "FALSE" . . "Master in Space Science and Technology"@en . . "https://hub.ucd.ie/usis/!W_HU_MENU.P_PUBLISH?p_tag=PROG&MAJR=F060 and https://www.ucd.ie/physics/spacescience/" . "90"^^ . "Presential"@en . "This programme is ideal for graduates of Physics, Engineering and closely related disciplines, who want to transfer their expertise to the fast-growing global space sector. Ireland is a member of the European Space Agency (ESA) and dozens of Irish companies and researchers are involved in major international space missions. UCD is building Ireland’s first satellite, EIRSAT-1.\n\nCourse highlights include a hands-on CubeSat lab, payload development and satellite systems engineering of a high-altitude balloon experiment and participation in an international mission design team project. A 3-month internship provides relevant training for industry or research and can lead to employment. Students have completed internships at the European Astronaut Centre (EAC), ESA, NASA-Ames, Cosine, ENBIO, InnaLabs, Skytek, Eblana Photonics and Réaltra.\nProgramme Outcomes:\nDescribe the state-of-the-art of knowledge in space science and technology\nApply acquired knowledge and technical skills in the space industry, or in graduate research\nDraw on a suite of relevant professional and transferable skills\nEngage actively in professional networking within the field \nParticipate constructively in multi-disciplinary, international teams"@en . . . . . . . . "1"@en . "FALSE" . . "Master"@en . "None" . "9560.00" . "Euro"@en . "27720.00" . "Mandatory" . "Our MEng Aerospace Engineering degree will equip you with industry knowledge and an in-depth understanding of the aerospace design and build process. Study materials and manufacturing, stress and dynamics, energy and thermodynamics to gain a solid grounding in aerospace engineering principles.\n\nGraduate ready to take up your place within the exciting, fast-paced aerospace industry. You'll develop core skills that you'll take with you through your career, such as innovation, teamwork and creativity.\n\nBy the end of the course, you'll be prepared for employment in leading aerospace companies such as Airbus UK, BAE Systems, Rolls-Royce, Leonardo, MBDA, Boeing and GE Systems.\n\nThere's an increasing demand for qualified aerospace engineers in the industry, so you'll have strong employability prospects. Past graduates have gone into careers in the design and manufacture of civil and military aircraft, helicopters and jet engines.\n\nIn your second year, you'll have the chance to specialise through the Systems, Design and Manufacturing pathways, allowing you to follow your career aspirations.\n\nThroughout your course, you'll benefit from a range of professional opportunities. Get an inside track on the industry through regular factory tours and professional briefings from leading aerospace organisations and work on placements to build up valuable experience and professional skills."@en . "1"^^ . "TRUE" . "Upstream"@en . . . . . . . . . . . . . . . . . . "Physics, Space Science, Nanotechnology, Biological & Medical Physics"@en . .