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4.8.1 Description of the programme
A graduate from the cross-disciplinary master programme in Computational Science has a spectrum of knowledge ranging from some field or fields in science to the development and analysis of modern computational methods and software in Scientific Computing. The syllabus of the program corresponds to this span of fields. The program offers a range of courses that will lead to a masters degree with a major in Computational Science, and normally with a specialisation in an area in Science.
The first part of the initial semester is partly used to assure that students with diverse bachelor degree backgrounds arrives at a common knowledge base, for example through an individually constructed bridging course in Scientific Computing and/or Programming. During the second part of the initial semester and during the second semester, mainly courses at the advanced level in Scientific Computing, Biology, Physics, Earth Science and Chemistry are given. During the final year, courses with a strong connection to research and development in academia and in society are given. The master thesis project can be performed during the last semester, or in parallel with other courses during the whole second year.
4.8.2 Comprehensive aims of the education
The master programme in Computational Science results in a combination of knowledge and abilities in some area in Science and in Scientific Computing. The programme gives a student with a Bachelor degree in Science or in Mathematics/Scientific Computing/Computer Science deeper knowledge in some are in Science combined with knowledge on modern computational techniques and ability of using such techniques for solving problems in Science. The cross-disciplinary education results in knowledge and abilities suitable for advanced assignments in trade and industry, public authorities and institutions, business, or for further studies towards the PhD degree in a variety of fields. A graduated student from the programme should be able to organise and run research and development projects in many fields.4.8.3 Aims as expected results of the study
Knowledge and understanding
Within the frame of objectives stated in the Higher Education Ordinance (see chapter 2) graduated students should
- have extensive knowledge on principles, methodologies and algorithms for computer simulations and computations based on mathematical models, and an ability to apply this knowledge within at least one field in Science
- have extensive knowledge within at least one field in Science, including both a breadth of knowledge covering the full field and specialist knowledge in some parts of the field.
Skills and abilities
Within the frame of objectives stated in the Higher Education Ordinance (see chapter 2) graduated students should
- have an ability to critically and systematically integrate knowledge from Scientific Computing and at least one are in Science, and an ability to analyse, assess, and handle complex phenomenon and issues in this field, also in situations where only limited information is available.
- be well acquainted with the scientific literature in Scientific Computing and at least one area in Science
- have an ability to use advanced computational software and different classes of computer systems for solving computational problems in Science
- have an ability to understand and use mathematical models for describing phenomena in Science
- be able to participate in scientific and development work and to work independently in other qualified settings
- have an ability to work in collective environments, including groups of cross-disciplinary character, and an ability to lead work in groups
Judgement and approach
Within the frame of objectives stated in the Higher Education Ordinance (see chapter 2) graduated students should
- be able to critically, independently and creatively identify and formulate problems and to plan and pursue advanced tasks within given timeframes, using adequate mathematical models, software and computer systems
- be able to validate and assess results from computer simulations and numerical computations
- be able to, in a scientific and popular way, describe the current knowledge base both within the field of specialisation in Science and in Scientific Computing
4.8.4 Programme outline
The programme results in a specialisation in Biology, Physics, Earth Science, Chemistry or Computational Science. Some of the courses are taken jointly with students from other programmes.4.8.5 The courses of the programme
The order of courses in the programme is shown below per study year. Each year is divided into four periods, period 11 means period one year one etc. Main field is given by the abbreviations D = Computer Science, F = Physics, K = Chemistry M = Mathematics T = Technology and TB = Computational Science. Courses written in italics are eligible courses. Courses, up to 30 hp, also can be chosen from other main fields.
Year 1
| Course code | Course name | Hp | Level | Main field |
| Period 11 | ||||
| 1TD044 | Scientific Computing, Bridging course | 10 | D | TB D M |
| 1MA060 | Applied Mathematics | (5) | D | M |
| 1TD183 | Optimization NV1 | 7.5 | C | TB D |
| Period 12 | ||||
| Optional course/s | ||||
| 1TD046 | Programming, bridging course | 10 | D | D TB |
| 1MA060 | Applied Mathematics | (5) 10 | D | M |
| 1MS009 | Computer-intensive Statistics and Data Mining | 10 | D | M |
| 1DL300 | Database Design I | 5 | C | D |
| 1TD242 | Analysis of Numerical Methods NV1 | 7.5 | D | TB D |
| 1DL025 | Data Mining | 7.5 | D | TB D M |
| 1TD389 | Scientific Visualization | 5 | D | D TB |
| Period 13 | ||||
| 1TD379 | High Performance Computing and Programming NV1 | 7.5 | C | TB D |
| Optional course/s | ||||
| 1DL400 | Database Design II | 5 | D | D T |
| 1DL250 | Software Engineering | 5 | D | D T |
| 1TD388 | Computer Graphics | 10 | D | D TB |
| 1TD247 | Applied Scientific Computing NV1 | 7.5 | D | TB |
| 1FA240 | Computational Physics | 7.5 | D | TB F |
| 1TD252 | Finite Element Methods NV1 | 7.5 | D | TB D M |
| Period 14 | ||||
| 1TD480 | Programming of Parallel Computers | 10 | D | TB D |
| Optional course/s | ||||
| 1MA256 | Modelling Complex Systems | 10 | D | M TB |
| 1TD908 | Degree project D in Computational Science | 15 | D | TB |
| Period 21 | Hp | Nivå | Område | |
| 1TD309 | Project in Computational Science | 15 | D | D TB |
| 1KB550 | Chemical Bonding and Computational Chemistry | (5) | D | K |
| Period 22 | ||||
| Optional course/s | ||||
| 1DL025 | Data Mining | 7.5 | C | D |
| 1DL300 | Database Design I | 5 | C | D |
| 1KB550 | Chemical Bonding and Computational Chemistry | (5) 10 | D | K |
| 1TD242 | Analysis of Numerical Methods NV1 | 7.5 | D | D TB |
| Period 23 | ||||
| 1TD808 | Degree project E in Computational Science | 30 | E | TB |
| Period 24 | ||||
| 1TD808 | Degree project E in Computational Science | 30 | E | TB |
4.8.6 Eligibility requirements
A Bachelor of Science degree in Science, Engineering, Mathematics or Computer Science, inclucing at least- one semester (30 ECTS credits) in Mathematics, including Algebra, Linear Algebra, Calculus and Vector Calculus,
- one five weeks course (7.5 ECTS) in programming and one five weeks course in numerical methods (numerical analysis or Scientific Computing)
Proficiency in English. Students who, outside the programme, have acquired equivalent qualifications corresponding to at least 15 hp on advanced level in addition to the degree at bachelor’s level, may apply to be accepted to a later part of the programme. The application deadline is for the autumn term May 1 and for the spring term December 1.
4.8.7 Grade and examination
Unless otherwise prescribed in the course syllabus, a grade is to be awarded on completion of a course. A student who has taken two examinations in a course or a part of a course without obtaining a pass grade is entitled to have another examiner appointed, unless there are special reasons to the contrary.4.8.8 Courses together in a degree
Some courses cannot be considered in a degree together. Which courses this concern will be pointed out in each course syllabus.4.9.9 Eligibility requirements
Upon request, a student who has received a pass grade in a course is to receive a course certificate from the higher education institution. Upon request, a student who meets the requirements for a qualification is to receive a diploma from the higher education institution.
A Degree of Master (One Year) is obtained after the student has completed course requirements of 60 higher education credits with a certain area of specialisation determined by each higher education institution itself, including at least 30 higher education credits with in-depth studies in Computational Science. For a Degree of Master (One Year) students must have completed an independent project (degree project) worth at least 15 higher education credits in Computational Science, within the framework of the course requirements.
A Degree of Master (Two Years) is obtained after the student has completed course requirements of 120 higher education credits with a certain area of specialisation determined by each higher education institution itself, including at least 60 higher education credits with in-depth studies in Computational Science. For a Degree of Master (Two Years) students must have completed an independent project (degree project) worth at least 30 higher education credits in Computational Science, within the framework of the course requirements. A degree of Master (Two Years) may, except for courses on advanced level, contain one or several courses on basic level comprising not more than 30 higher education credits