Programme syllabus for the master programme in Computational Science 2015/2016

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 M.Sc. 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 ensure that students with diverse bachelor degree backgrounds arrive 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 skills in some area of Science and in Scientific Computing. The programme is intended for students with a Bachelor degree in Science or in Mathematics/Scientific Computing/Computer Science and provides deeper knowledge in some area of 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 skills suitable for advanced assignments in trade and industry, public authorities and institutions, business, or for further studies towards the Ph.D. 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 framework of objectives stated in the Higher Education Ordinance (see Chapter 2) graduated students should
• Demonstrate deepened knowledge and understanding within at least one field of Science, including a breadth of knowledge in this field as well as specialist knowledge in some parts of the field and insight into current research and development activities.
• Demonstrate deepened 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

Skills and abilities
Within the framework of objectives stated in the Higher Education Ordinance (see Chapter 2) graduated students should
• Demonstrate an ability to critically and systematically integrate knowledge from Scientific Computing and at least one area of Science, and an ability to analyse, assess, and address complex phenomena and issues in this field, also in situations where only limited information is available
• Demonstrate an ability 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
• Demonstrate an ability to give oral as well as written reports of and discussion of their conclusions in applied scientific computing, and of the knowledge and the arguments on which these are based, and to do this in dialogue with various groups both nationally and internationally
• Demonstrate an ability to use advanced computational software and different classes of computer systems for solving computational problems in Science and Engineering
• Demonstrate an ability to understand and use mathematical models for describing phenomena in Science and Engineering
• Demonstrate skills required to participate in research and development activities and to work independently in other qualified settings within Computational Science

Judgement and approach
Within the frame of objectives stated in the Higher Education Ordinance (see chapter 2) graduated students should
• Demonstrate an ability to validate and assess results from computer simulations and numerical computations
• Demonstrate an ability to make judgements within Computational Science, taking into account relevant scientific, societal, and ethical aspects as well as demonstrating an awareness of ethical aspects of research and development
• Demonstrate insight about the potential and limitations of Computational Science, its role in society and human responsibility for its use
• Demonstrate an ability to identify their need for further knowledge in Computational Science and to assume responsibility for further developing their own knowledge

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 can be seen from the outline.4.8.1 Description of the programme

This is a multidisciplinary master program with the main aim to educate students within the field of Applied Biotechnology. Students in the program will get theoretical and practical competence within the broad field of Applied Biotechnology. Furthermore, students will also get basic knowledge in business economics and project management.

All students within the program study the same courses during the first year. During the second year students choose among the suggested courses, combined with a degree project of 30 hp or 45 hp. Students will get a good grounding for future work in both academia and industry in Sweden or internationally. Depending on specialization, students will get the opportunity to work with research and development concerning e.g. protein-based drugs, diagnostic tools and databases. The degree project can be done in industry, university or at a governmental institution.

Year 1

Period

Hp

Level

Main Field

11

1TD045

Scientific Computing, Bridging Course

5

A1N

TB D

1TD389

Scientific Visualization

5

A1N

TB D T

1TD397

Scientific Computing III

5

A1N

TB D T

Eligible Courses1

1FA352

Quantum Mechanics [BF]

(5)

A1N

F

1MA148

Applied Mathematics [MS]

5

A1N

M

1TD046

Programming, Bridging Course

(5)

A1N

D T

1DL301

Database Design I

5

G2F

D T

12

1TD184

Optimization

5

A1N

TB D T

1TD253

Finite Element Methods

5

A1F

TB D T

Eligible Courses

1FA352

Quantum Mechanics [BF]

(5) 10

A1N

F

1MA151

Applied Dynamical Systems [MS]

5

G1F

M

1DL400

Database Design II

5

A1N

D, T

1TD396

Computer Assisted Image Analysis I

5

A1N

D T

1DL600

Software Testing and Maintenance

(5)

A1N

D T

1TD046

Programming, Bridging Course

(5) 10

A1N

D T

1KB550

Chemical Bonding and Computational Chemistry

10

A1N

K

1MA209

Financial Derivatives

7,5

A1F

M

13

1TD480

Programming of Parallel Computers

10

A1N

TB D T

1TD245

Research Training in Scientific Computing

(2,5)

A1F

TB D

Eligible Courses

1FA573

Computational Physics [BF]

5

A1N

TB F

1DL250

Software Engineering

5

A1N

D T

1TD398

Computer Assisted Image Analysis II

10

A1F

D T

1DL600

Software Testing and Maintenance

(5) 10

A1N

D T

1DT064

Distributed Systems

5

A1N

D T

1MS012

Markov Processes

10

A1N

M

14

1TD351

High Performance Computing and Programming

5

A1N

TB D T

1TD254

Finite Element Methods

5

1TD245

Research Training in Scientific Computing

(2,5)5

A1F

TB D

Eligible Courses

1MA256

Modelling of Complex Systems [MS]

10

A1N

TB M

1KB206

Computational Chemistry

5

G2F

TB K

1KB273

Computational Quantum Chemistry for Molecules and Materials

10

A1F

K

1TD188

Computational Finance - Calibration and Estimation

5

A1F

TB D

1TD267

Large Datasets for Scientific Applications

5

A1F

TB D T

1TD388

Computer Graphics

10

A1N

D T

1TD204

Software Architecture with Java

5

A1N

D T

1ME406

Numerical modelling of the Atmosphere

10

A1N

TB G

1TD908

Degree Project D in Computational Science2

15

TB

1Courses may be chosen from the list of eligible courses or amont other courses of relevance for the programme, provided that the student fulfils the prerequisites for the course.

2This course is intended for students who plan to finish the programme studies after one year and apply for a 60 hp degree.


Year 2

Period

Hp

Nivå

Område

21

1TD248

Applied Scientific Computing

5

A1F

TB D T

Eligible Courses

1MB415

Discrete Computational Biology [BK]

5

A1F

BK T

1KB362

Statistical Thermodynamics: Theory and Simulation Methods

(5)

A1F

TB K F

 

1TD186

Computational Finance - Pricing and Valuation

5

A1F

TB D

1TD243

Analysis of Numerical Methods

5

A1F

TB D T

1DT052

Computer Networks I

5

G1F

D T

1TD265

Applied Cloud Computing

10

A1N

TB D

1DL360

Data Mining

5

A1N

D T

1MS025

Stationary Stochastic Processes

5

A1N

M

22

Eligible Courses

1TD307

Project in Computational Science [BF; BK; MS]

15

A1F

TB D T

1KB362

Statistical Thermodynamics: Theory and Simulation Methods

(5) 10

A1F

TB K F

1MB416

Knowledge-based Systems in Bioinformatics [BK]

5

A1N

BK T

1FA357

Statistical Methods in Pysics [BF; odd years only]

5

A1N

F

1MS009

Computer Intensive Statistics and Data Mining

10

A1N

M

23

1TD808

Degree Project E in Computational Science

(15)

A2E

TB

24

1TD808

Degree Project E in Computational Science

(15) 30

A2E

TB

4.8.6 Eligibility requirements

A bachelor of Science degree (equivalent to a Swedish degree of at least 180 credits, i.e. three years of full-time study), in Science, Engineering, Mathematics or Computer Science, including at least 30 credits in Mathematics, including Algebra, Linear Algebra, Calculus and Vector Calculus, 5 credits in programming and 5 credits in numerical methods (numerical analysis or Scientific Computing). Proof of skills in English to a level corresponding to English B in the Swedish secondary school. This is normally attested by means of an internationally recognised test with the following minimum scores:
•IELTS: an overall mark of 6.5 and no section below 5.5
•TOEFL: Paper-based: Score of 4.5 (scale 1-6) in written test and a total score of 575. Internet-based: Score of 20 (scale 0-30) in written test and a total score of 90
•Cambridge: CAE, CPE

Students, who have acquired equivalent qualifications outside the programme, 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 for the autumn term is 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.

4.8.9 Degree and diploma

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