Master's Programme in Life Science Technologies
Curriculum 2020–2022
Master of Science (Technology) degree is 120 ECTS credits. Master’s Programme in Life Science Technologies consists of major studies (60–65 ECTS), elective studies (25–30 ECTS), and master’s thesis (30 ECTS). Programme includes 15 ECTS of common studies for all students. Common studies are included in the major studies.
- Major studies (60–65 cr)
- Master's thesis (30 cr)
- Electives (25–30 cr)
Master's Programme in Life Science Technologies offers a multidisciplinary curriculum focusing on important aspects of current and emerging technologies for life sciences, covering fields such as biological data analysis and modeling, advanced biomaterials and bioelectronics, biomedical engineering and neuroscience. The programme draws on fundamental and applied knowledge on these fields, and is closely linked to research conducted in the participating schools and departments.
To prepare the graduates for their future work with large and often complex systems, the programme includes practical project works in groups, which provide skills for solving multifaceted and ill-defined problems similar to those faced in the actual professional life. These projects typically include experimental and practical components as well as fundamental theoretical aspects.
The programme also gives the student a comprehensive foundation for doctoral studies. The Bioinformatics and Complex Systems majors host a doctoral track with a selective student intake.
Majors 2020–2022
Master's Programme in Life Science Technologies offers six majors for specializing in Bioinformatics and Digital Health, Biomedical Engineering, Biosensing and Biolectronics, Biosystems and Biomaterials Engineering, Complex Systems or Human Neuroscience and Technology.
Typically, a major consists of compulsory courses and of optional courses. 15 credits of compulsory courses are common to all majors.
Bioinformatics and Digital Health
Professor in charge: Harri Lähdesmäki
Extent: 60 credits
Abbreviation: BIOINFO
Code: SCI3092
The Bioinformatics and Digital Health major in the Life Science Technologies programme is designed to give a strong competence in bioinformatics and biomedical/health data analysis methods. The major offers a solid background in probabilistic modeling, machine learning and data science to understand the methodological basis of bioinformatics and computational biology methods. The major gives skills and tools to develop new computational methods and models and to apply them to real world biomolecular and health data. Computer practicals are part of most courses ensuring understanding of both theory and practice of the methods.
State-of-the-art methods for analyzing next-generation sequencing and other 'omics data as well as biological networks are part of the curriculum. Examples of research questions studied include predicting drug-target interactions, reconstructing biological networks, identifying disease biomarkers from biomedical and health data, and modelling dynamical behavior of complex biological systems.
Mathematics, statistics and computer science enthusiastic students are recommended to broaden their expertise in statistical data analysis, machine learning and artificial intelligence even further with additional optional courses or with an elective minor from Machine Learning, Data Science and Artificial Intelligence. Similarly, elective minor from software systems and engineering nicely complements this major. Knowledge of biology and biological systems can be extended with an elective minor.
For the Bioinformatics and Digital Health major (60 ECTS credits) the students have to take the common compulsory courses of the Life Science Technologies programme (15 cr), compulsory major subject courses (15+15 cr), and choose additional courses (15 cr) from the compulsory or optional course lists given below. To complete the degree, students take language studies (3 cr) and elective courses (minimum 27 cr). For elective courses, a minor subject, an international mobility period or an internship is recommended.
| Code | Course name | ECTS credits | Period/Year |
|---|---|---|---|
| Compulsory courses of the programme (15 credits): | |||
| MS-C1620* | Statistical Inference* | 5 | III-IV/1 |
| JOIN-E3000 | Life Science Technologies Project Course | 10 | III-V/1 |
| Compulsory courses of the major ( minimum of 30 credits): | |||
| A. Courses on bioinformatics and digital health (choose minimum of 15 credits): | |||
| CS-E5865 | Computational Genomics | 5 | I/1 |
| CS-E5875 | High-throughput Bioinformatics | 5 | II/1 |
| CS-E5885 | Modelling Biological Networks | 5 | III/1 |
| CS-E5890** | Statistical Genetics and Personalised Medicine** | 5 | IV-V/1* |
| CS-E4880** | Machine Learning in Bioinformatics** | 5 | IV-V/1* |
| CHEM-E8120 | Cell Biology | 5 | II/1 |
| B: Courses on probabilistic modeling and machine learning (choose minimum of 15 credits): | |||
| CS-E4710 | Machine Learning: Supervised methods | 5 | I-II/1 |
| CS-E5710 | Bayesian Data Analysis | 5 | I-II/1 or 2 |
| CS-E4890 | Deep Learning | 5 | IV-V/1 |
| CS-E4820 | Machine Learning: Advanced Probabilistic Methods | 5 | III-IV/1 |
| CS-E4840 | Information Visualization | 5 | IV-V/1 |
| CS-E4830 | Kernel Methods in Machine Learning | 5 | IV-V/1 |
| Optional courses of the major (choose courses to fulfill the 60 credit requirement): | |||
| CS-E4875 | Research Project in Machine Learning, Data Science and Artificial Intelligence | 5-10 | I-V/1 or 2 |
| CS-E3190 | Principles of Algorithmic Techniques | 5 | I-II/1 |
| CS-E4800 | Artificial Intelligence | 5 | III-IV/1 |
| CS-E5795 | Computational Methods in Stochastics | 5 | I-II/1 or 2 |
| CHEM-E8125 | Synthetic Biology | 5 | IV-V/1 |
| CHEM-E3170*** | Systems Biology*** | 5 | IV-V/1*** |
| NBE-E4150 | DNA Nanotechnology | 5 | I-II/1 or 2 |
*If the student has already taken MS-C1620 in the Bachelor degree or has the corresponding knowledge and skills, he/she should instead of MS-C1620 take a more advanced statistics course or a MSc-level data-modeling course, for example MS-E2112, CS-E4710, or CS-E5710.
** CS-E5890 Statistical Genetics and Personalised Medicine and CS-E4880 Machine Learning in Bioinformatics are lectured every second year (alternating). CS-E4880 Machine Learning in Bioinformatics is lectured in 2020-2021 and CS-E5890 Statistical Genetics and Personalised Medicine is lectured in 2021-2022.
*** Lectured in even years
Recommendations for elective studies for Bioinformatics and Digital Health
For the elective studies to accompany the Bioinformatics and Digital Health major, it is recommended to take a minor subject or an international mobility period or an internship. The autumn period of second year is the recommended time period for elective studies.
Recommended minors
The following minors given in Aalto University are recommended:
Life Science Technologies programme minors:
- Biomedical engineering
- Biosensing and Biolectronics
- Complex systems
- Human Neuroscience and Technology
Computer, Communications, and Information Science programme minors:
- Machine Learning, Data Science and Artificial Intelligence
In addition, University of Helsinki (www.helsinki.fi) offers courses suitable for elective studies in
- Algorithmic bioinformatics
- Molecular biosciences
- Biomedicine
Courses offered by University of Helsinki can be taken through the Flexible Study Rights (JOO) agreement (www.joopas.fi).
Recommendations for international mobility
An international mobility period of approximately one semester is recommended. The suitable timing for mobility is Autumn period of the second study year.
Biomedical Engineering
Professor in charge: Matias Palva
Extent: 65 credits
Abbreviation: BME
Code: SCI3059
Biomedical engineering builds on a solid basis of physics and technology to characterize, monitor, image and influence biological systems. This major introduces the student to physics of biological systems and to key concepts of related imaging and signal analysis. In addition, the major provides knowledge and skills for developing novel engineering solutions for diagnostic and treatment needs in health care. Biomedical Engineering major offers excellent foundations for pursuing a career in medical technology industry or in academia.
For the major (65 ECTS credits) the students take the common courses of the Life Science Technologies programme (15 cr), compulsory major subject courses (25 cr), and choose optional courses (25 cr) from the course list given below. To complete the degree, students take elective courses (minimum 25 cr). For elective courses, a minor subject or an international mobility period is recommended.
| Code | Course code | ECTS credits | Period/Year |
|---|---|---|---|
| Compulsory common courses of the programme (15 credits): | |||
| MS-C1620 | Statistical Inference | 5 | III-IV/1* |
| JOIN-E3000 | Life Science Technologies Project Course | 10 | III-V/1 |
| Compulsory courses of the major (25 credits): | |||
| NBE-E4000 | Principles of Biomedical Imaging | 5 | I-II/1 |
| NBE-E4050 | Signal Processing in Biomedical Engineering | 5 | I-II/1 |
| NBE-E4100 | Molecular Biophysics | 5 | III-V/1 |
| NBE-E4510 | Special Assignment in Biomedical Engineering | 10 | I-V/1, summer |
| Optional courses of the major (choose 25 credits): | |||
| NBE-E4010 | Medical Image Analysis | 5 | I-II O |
| NBE-E4020 | Medical Imaging | 5 | III-IV E |
| NBE-E4045 | Functional Brain Imaging | 5 | I-II/2 |
| NBE-E4120 | Cellular Electrophysiology | 5 | I-II E |
| NBE-E4130 | Information Processing in Neural Circuits | 5 | III-V O |
| NBE-E4140 | Neurophysics | 5 | IV-V E |
| NBE-E4150 | DNA Nanotechnology | 5 | I-II/2 |
| NBE-E4210 | Structure and Operation of the Human Brain | 5 | I-II/1 or 2 |
| NBE-E4250 | Mapping, Decoding and Modeling the Human Brain | 5 | III/ 1 or 2 O |
| NBE-E4260 | Genesis and Analysis of Brain Signals | 5 | III-IV/1 or 2 |
| NBE-E4300 | Medical Device Innovation | 5 | III-V E |
| NBE-E4310 | Biomedical Ultrasonics | 5 | I-II/1 or 2 |
E = lectured in even years
O = lectured in odd years
* If the student has already taken MS-C1620 in the Bachelor degree or has the corresponding knowledge and skills, he/she should instead of MS-C1620 take a more advanced statistics course or a MSc-level data-modeling course, for example MS-E2112, CS-E4710, or CS-E5710.
Recommendations for elective studies for Biomedical Engineering
For the elective studies to accompany the biomedical engineering major, it is recommended to take a minor subject, an international mobility period or an internship. The autumn term of second year is the recommended time for international mobility.
Recommended minors
The following minors given in Aalto University are recommended:
Life Science Technologies programme minors:
- Bioinformatics and Digital Health
- Biosensing and Bioelectronics
- Biosystems and Biomaterials Engineering
- Complex Systems
- Human Neuroscience and Technology
Recommendations for international mobility
An international mobility period of approximately one term is recommended. The suitable timing for mobility is the autumn term of the second study year.
Biosensing and Bioelectronics
Professor in charge: Tomi Laurila
Professors: Mervi Paulasto-Kröckel, Ilkka Tittonen, Simo Särkkä, Ilkka Laakso, Ivan Vujaklijan
Extent: 60 credits
Abbreviation: -
Code: ELEC3045
The target is to educate engineering experts who have versatile comprehension of detection, processing and analyses of biosignals from various sources. To accomplish this the student is introduced to nanoscale phenomena, microfabrication techniques, biomaterials science, biochemical recognition of biomolecules, physical transducers, sensor technologies and to good extent to various clinical equipment. The basic knowledge needed in the development of innovations in the field of biosensors and bioelectronics are provided. The students are also strongly encouraged to consider practical aspects and possible applications of their knowhow throughout their studies.
This major combines studies both in the theory and practice needed to design, develop, fabricate and characterize biosensors, biomedical devices and medical instrumentations. Hands on experience is gained to understand the biocompatibility of both organic and inorganic materials used in electronics as well as the interactions between low frequency electromagnetic fields and living tissue and in special applications these interactions even with single cells and biomolecules.
The major has been structured to allow for flexibility, and the student may emphasize chosen areas of interest. In addition to courses common to all Life Science Technologies masters (15 cr), the major has four mandatory courses (total of 20 cr). If the student has already taken MS-C1620 in the Bachelor degree or has the corresponding knowledge and skills, he/she should instead of MS-C1620 take a more advanced statistics course or a MSc-level data-modeling course, for example MS-E2112, CS-E5710, or CS-E4710 or alternatively some course from the optional courses offered by the major.
Further, there are three thematic modules, each containing 25 cr: (1) Signal processing in biosciences, (2) Micro- and nanofabrication and (3) Biomaterials and electrochemistry. These are only suggestions and thus in addition to existing modules student can also compile his/hers own module or modify the existing ones.
Course descriptions are available in Sisu.
| Code | Course name | ECTS credits | Period/Year |
|---|---|---|---|
| Mandatory common courses of the programme (15 credits): | |||
| MS-C1620 | Statistical inference | 5 | III-V/1 |
| JOIN-E3000 | Life Science Technologies Project Course | 10 | III-V/1 |
| Mandatory courses of the major (20 credits): | |||
| ELEC-E8729 | Biomaterial Interfaces | 5 | I-II/1 |
| ELEC-E8726 | Biosensing | 5 | III-IV/1 |
| ELEC-E3260 | Biomolecules | 5 | III/1 |
| ELEC-E8734 | Biomedical Instrumentation | 5 | II/1 |
| Optional courses ( 25 credits): | |||
| ELEC-E0210 | Master's Thesis Process | 2 | I-III |
| Modules: | |||
| 1. Signal processing in biosciences (Choose 25 cr) | |||
| ELEC-C5212 | Introduction to Statistical Signal Modelling | 5 | IV-V |
| ELEC-E8739 | AI in health technologies | 5 | I-II |
| ELEC-E9111 | Mathematical Computing | 5 | I-II |
| CS-E4710 | Machine Learning: Supervised methods | 5 | I-II |
| ELEC-E7260 | Machine Learning for Mobile and Pervasive Systems | 5 | III-IV |
| ELEC-E8743 | Neurorobotics | 5 | III-IV |
| 2. Micro- and nanofabrication (Choose 25 cr) | |||
| CHEM-E5115 | Microfabrication | 5 | IV-V |
| CHEM-E8135 | Microfluidics and BioMEMS | 5 | III-IV/1 |
| ELEC-E3230 | Nanotechnology | 5 | IV |
| ELEC-E3280 | Micronova Laboratory Course | 5 | I-II |
| ELEC-E3220 | Semiconductor Devices | 5 | III/1 |
| NBE-E4150 | DNA Nanotechnology course | 5 | I-II |
| NBE-E4100 | Molecular Biophysics | 5 | III-V |
| 3. Biomaterials and electrochemistry (Choose 25 cr) | |||
| ELEC-E8724 | Biomaterials Science | 5 | I-II |
| ELEC-E8725 | Methods of Bioadaptive Technology | 5 | I-II |
| CHEM-E4106 | Electrochemistry P | 5 | III/1 |
| CHEM-E4107 | Laboratory Work in Electrochemistry | 5 | IV/1 |
| CHEM-E4235 | Transport Processes at Electrodes and Membranes | 5 | I-II/2 |
| NBE-E4150 | DNA Nanotechnology course | 5 | I-II |
| NBE-E4100 | Molecular Biophysics | 5 | III-V |
Biosystems and Biomaterials Engineering
Professor in charge: Alexander Frey
Extent: 60 credits
Code: CHEM3028
The major in Biosystems and Biomaterials Engineering in the Life Science Technologies programme is designed to give graduates a broad training and in-depth knowledge, combined with practical experience. Starting from the understanding of basic biological phenomena, three distinct tracks are offered that combine biosciences with computational biology, biomaterials or chemistry. The major also supports the development of transferable skills such as organizing personal and teamwork, working as part of a team, and effective communication of scientific knowledge to a forum of peers and experts and to the general public.
In the biosystems engineering track graduates obtain a strong knowledge of engineering of cellular system (cell factories, synthetic biology) and computational biology methods and the skills to apply this knowledge in the fields of biotechnology.
The track focuses on:
- Engineering of cellular systems through the use of genetic engineering and synthetic biology for programming of genetic circuits and cellular pathways
- Computational analysis of genomic and modern high-throughput biological data with applications, and computational and statistical methods for modelling of biological networks and systems.
In the chemistry of life track graduates obtain a broad and balanced training in the disciplines bridging the life sciences with chemistry.
The track focuses on:
- structure and reactivity of small organic building blocks,
- their molecular interactions with biomacromolecules (proteins, nucleosides,...) as basis for both pharmaceutical research and bioprocess development,
- and the implications thereof for synthetic biology and cellular factories.
In the biomaterials trackgraduates obtain a broad and balanced training in material, their biophysical properties and characterization methods and their applications in life sciences.
The track focuses on:
- Synthesis, purification of synthetic and biopolymers using chemistry and enzymes
- molecular level phenomena and biophysical properties of materials.
The major comprises common compulsory studies of 30 ECTS, of which 15 ECTS are common for all students in the Life Science Technologies Master’s Programme. Each track includes compulsory (20 ECTS) and elective studies (10 ECTS).
| Code | Course name | ECTS credits | Period/Year |
|---|---|---|---|
| Compulsory common courses of the programme (15 credits): | |||
| MS-C1620 | Statistical inference* | 5 | III-IV/1 |
| JOIN-E3000 | Life Science Technologies Project Course | 10 | III-V/1 |
|
*If the student has already taken MS-C1620 in the Bachelor degree or has the corresponding knowledge and skills, he/she should instead of MS-C1620 take a more advanced statistics course or a MSc-level data-modeling course, for example MS-E2112, CS-E4710, or CS-E5710. *For students of Biomaterials track track we recommend taking the course in second study year. |
|||
| Compulsory courses of the major (15 credits): | |||
| CHEM-E3190 | Metabolism D | 5 | I-II/1 |
| CHEM-E8110 | Laboratory Course in Biosystems and Biomaterials Engineering | 5 | I-II/1 |
| CHEM-E8120 | Cell Biology | 5 | II/1 |
| Biosystems engineering track (30 credits): | |||
| CS-E5865 | Computational Genomics | 5 | I/1 |
| CS-E5875 | High-throughput Bioinformatics | 5 | II/1 |
| CHEM-E8115 | Cell Factory D | 5 | III/1 |
| CHEM-E8125 | Synthetic Biology | 5 | IV-V/1 |
| Select two of the following courses: | |||
| CS-E5885 | Modeling Biological Networks | 5 | III/1 |
| CHEM-E3120 | Microbiology | 5 | I/2 |
| CHEM-E3150 | Biophysical chemistry D | 5 | III/1 |
| CHEM-E3170 | Systems biology | 5 | IV-V/1 or 2, even years |
| CHEM-E8135 | Microfluides and BioMEMS D | 5 | III-IV/1 |
| Biomaterials track (30 credits): | |||
| CHEM-E2100 | Polymer Synthesis | 5 | I/1 |
| CHEM-E2130 | Polymer Properties | 5 | II/1 |
| CHEM-E3150 | Biophysical chemistry D | 5 | III/1 |
| CHEM-E8145 | Polymers in Medical Technology | 5 | III/1 |
| Select two of the following courses: | |||
| CHEM-E4210 | Molecular Thermodynamics D | 5 | II/2 |
| CHEM-E8100 | Organic Structural Analysis | 5 | I/2 |
| NBE-E4150 | DNA Nanotechnology | 5 | I-II/2 |
| Chemistry of life track (30 credits): | |||
| CHEM-E8100 | Organic Structural Analysis | 5 | I/1 |
| CHEM-E4160 | Reactivity of Aromatics | 5 | II/1 |
| CHEM-E4220 | Medicinal Chemistry D | 5 | II/2 |
| CHEM-E4109 | Modern Methods in Biocatalysis D | 5 | IV/1 |
| Select two of the following courses. | |||
| CHEM-E3150 | Biophysical chemistry D | 5 | III/1 |
| CHEM-E4195 | Selectivity in Organic Synthesis | 5 | IV/1 |
| CHEM-E4295 | Asymmetric Synthesis of Natural Products | 5 | I/2 |
| CHEM-E8115 | Cell Factory D | 5 | III/1 |
| CHEM-E8125 | Synthetic Biology | 5 | IV-V/1 |
Recommendations for elective studies
For the elective studies to accompany the major, it is recommended to take a minor subject.
The following minors offered in Aalto University are recommended:
Life Science Technologies programme minors:
- Bioinformatics
- Biosensing and Bioelectronics
- Complex Systems
- Human Neuroscience and Technology
- Biomedical engineering.
Suitable elective courses can be found from the list of elective specialization courses and selected courses from Bioinformatics or other majors in Life Science Technologies program.
Courses offered by University of Helsinki can be taken through the Flexible Study Rights (JOO) agreement (www.joopas.fi).
Complex Systems
Professor in charge: Professor Jari Saramäki
Extent: 60 credits
Abbreviation: CS
Code: SCI3060
The aim is to give the students a strong computational and theoretical background for understanding complex systems, from the human brain to a diversity of biological and social systems. The major has been structured such that the student can choose to emphasize the theory of complex systems or data science. Further, it is possible to add courses from other Life Science Technologies majors: e.g. the student can have a degree with 20 cr of data science and networks courses together with 25 cr of neuroscience courses. After completing their studies, the students have the necessary skills for interdisciplinary scientific careers, or, e.g. for data scientist positions in the industry.
The major has been structured to allow flexibility, and the student may emphasize chosen areas of interest. In addition to courses common to all Life Science Technologies masters, the major has a set of seven courses (35 cr) out of which at least five (25 cr) have to be chosen. After this, the student is free to choose the rest from two themes (Networks and Systems, Data Science and Machine Learning) as well as from other Life Science Technologies majors. It is, therefore, entirely possible to build a major that contains the fundamentals of complex systems and a number of neuroscience courses, or a combination of network science and machine learning, or a more mathematical networks track including courses from the department of mathematics. The student can also suggest other topics (economics, social sciences, etc); we are flexible and willing to tailor degrees that match the needs of the student.
| Code | Course name | ECTS credits | Period/Year |
|---|---|---|---|
| Compulsory common courses of the programme (15 credits): | |||
| MS-C1620 (*) | Statistical Inference | 5 | III-IV/1 |
| JOIN-E3000 | Life Science Technologies Project Course | 10 | III-V/1 |
| Compulsory courses of the major (pick at least 25 credits) | |||
| CS-E5740 | Complex Networks (recommended) | 5 | I-II/1 |
| CS-E5795 | Computational Methods in Stochastics | 5 | I-II/1 |
| MS-C2111 | Stochastic Processes | 5 | II/1 |
| CS-E5745 | Mathematical Methods for Network Science | 5 | III/1 |
| MS-E2112 | Multivariate Statistical Analysis | 5 | III-IV/1 |
| CS-E5755 | Nonlinear Dynamics and Chaos | 5 | III-IV/1 |
| CS-E5700 | Hands-on Network Analysis | 5 | IV-V/1 |
| Elective courses of the major (pick enough courses for 60 credits in total) | |||
| Theme I: Network and systems | |||
| CS-E5885 | Modeling Biological Networks | 5 | III/1 |
| MS-E1603 | Random Graphs and Network Statistics | 5 | I/2 |
| MS-E2122 | Nonlinear Optimization | 5 | I-II/1 or 2 |
| MS-E1602 | Large Random Systems | 5 | IV/1 (odd years) |
| MS-E1050 | Graph Theory | 5 | I/1 or 2 |
| CS-E4555 | Combinatorics | 5 | V/1 |
| CS-E5780 | Special Assignment in Complex Systems | 5-10 | I-V (on request) |
| CS-E5770 | Special Course in Complex Systems | 1-10 | I-V/1 |
| Theme II: Data science and machine learning | |||
| CS-E4840 | Information Visualization | 5 | IV-V/1 |
| CS-E4710 | Machine Learning, Supervised methods | 5 | I-II/2 |
| CS-E5710 | Bayesian Data Analysis | 5 | I-II/1 |
| CS-E4650 | Methods of Data Mining | 5 | I-II/1 or 2 |
| CS-E4890 | Deep Learning | 5 | IV-V/1 |
| CS-E4640 | Big Data Platforms | 5 | III-IV/2 |
| Theme III: pick any courses from other Life Science Technologies majors |
(*) If the student has already taken MS-C1620 in the Bachelor degree or has the corresponding knowledge and skills, he/she should instead of MS-C1620 take a more advanced statistics course or an MSc-level data-modeling course.
Recommendations for elective studies
In their elective studies, the students are encouraged to take courses from other majors of the LifeTech programme, according to their interests. Courses in the field of information and computer science are also recommended. Internship is also recommended in elective studies.
Human Neuroscience and Technology
Professor in charge: Lauri Parkkonen
Extent: 65 credits
Abbreviation: NEURO
Code: SCI3061
This major gives the students a strong background for understanding structure and function of the human brain, human cognition, as well as theoretical and practical knowledge of brain research methods and other neurotechnologies. After completing their studies, the students have an excellent background for a career in science and for applying their expertise in more applied fields such as medical technology, health and wellbeing, and game industry.
This major provides the students with up-to-date information on brain structure and function at different levels of analysis. Curriculum reflects the research interests of the faculty, who study sensory systems, cognitive functions, and develop brain research technologies. In modern neuroscience, command of advanced statistical and signal-analysis methods is mandatory; therefore, specific courses are included to cover this supporting field. The imaging courses of this major provide the students with an overview and hands-on practice of modern brain research methods.
Besides the regular lecture and course work, there is a compulsory special assignment, which exposes the student to a real research question and integrates him/her to a research team to learn the practices of the field and to develop his/her skills in reporting research.
| Code | Course name | ECTS credits | Period/Year |
|---|---|---|---|
| Compulsory common courses of the programme (15 credits): | |||
| MS-C1620 | Statistical Inference1) | 5 | III-IV/1 |
| JOIN-E3000 | Life Science Technologies Project Course | 10 | III-V/1 |
| Compulsory courses of the major (50 credits) | |||
| 1 Neuroscience and imaging (35 credits) | |||
| NBE-E4210 | Structure and Operation of the Human Brain | 5 | I-II/1 |
| NBE-E4000 | Principles of Biomedical Imaging | 5 | I-II/1 |
| NBE-E4225 | Cognitive Neuroscience | 5 | III/1 |
| NBE-E4240 | Advanced Course on Human Neuroscience | 5 | IV-V/1 |
| NBE-E4045 | Functional Brain Imaging | 5 | I-II/2 |
| NBE-E4520 | Special Assignment in Human Neuroscience | 10 | I-V, Summer/1 or 2 |
|
2 Analysis and modelling (5–10 credits) Select from the courses below. |
|||
| NBE-E4050 | Signal Processing in Biomedical Engineering | 5 | I-II/1 |
| CS-E5710 | Bayesian Data Analysis | 5 | I-II/2 |
| CS-E4710 | Machine Learning, Supervised methods | 5 | I-II/1 or 2 |
| CS-E5740 | Complex Networks | 5 | I-II/1 or 2 |
|
3 Supporting courses (5–10 credits). Select primarily from the ones below. Other relevant courses from other Life Science Technologies majors possible with an agreement of the responsible professor of the major. |
|||
| NEU-104 * | Integrative neurobiology (course at University of Helsinki) * | 5 | II-III/1 |
| NBE-E4120 | Cellular Electrophysiology | 5 | I-II (even years) |
| NBE-E4130 | Information Processing in Neural Circuits | 5 | III-V (odd years) |
| NBE-E4010 | Medical Image Analysis | 5 | I-II (odd years) |
| NBE-E4020 | Medical Imaging | 5 | III-IV (even years) |
| NBE-E4300 | Medical Device Innovation | 5 | III-IV (even years) |
| NBE-E4250 | Mapping, Decoding and Modeling the Human Brain | 5 | III (odd years) |
| NBE-E4260 | Genesis and Analysis of Brain Signals | 5 | III-IV |
1) If the student has already taken MS-C1620 in the Bachelor degree or has the corresponding knowledge and skills, he/she should replace MS-C1620 either with a more advanced statistics course such as MS-E2112 or an extra course from Analysis and Modelling category.
*Please note that this course is part of the curriculum and it is not handled as a credit transfer. If you have completed the course, send the transcript of records to your study coordinator and the course will be registered with the grade.
Recommendations for elective studies
Students are encouraged to take courses from other Majors of the Life Sciences Technologies programme, depending on own interests. Those who are especially interested in neurotechnologies, can extend their knowledge profile by taking courses from Biomedical Engineering major and Biosensing and Bioelectronics major. Internship is also recommended in elective studies.
Elective studies 25–30 cr
Students choose 25–30 credits of elective studies depending on the extent of the major. A minor and/or individual courses can be taken as elective studies. Individual courses can also be taken from other programmes at Aalto University or from other Finnish universities through Flexible Study Right (JOO). Students can also participate in an international student exchange programme or do an internship (1–10 ECTS) in Finland or abroad. Further information on Flexible Study Right and internship is available in Into:
Studies at other Aalto Schools or in Finnish Universities
Further information on minors at Aalto is available on the page Minors.
Mandatory language studies are included as part of the Finnish bachelor’s degree for students who have studied in Finland and whose language of education is Finnish or Swedish. If the mandatory language studies have not been completed in the Finnish bachelor’s degree, the student must take 2 credits in the second national language and 3 credits in one foreign language, including both oral (o) and written (w) proficiency, as part of their master’s degree.
Students who have received their education in a language other than Finnish or Swedish, or received their education abroad, are required to complete only 3 credits in one foreign language.
In the Master’s Programme in Life Science Technologies, English is recommended as the mandatory foreign language.
Students who have received their education abroad and who already have excellent command of English (e.g. as their native language) may choose 3 credits of Finnish courses instead, hence not covering the requirement of oral/written proficiency but meeting the language requirement of the degree. If this applies to you, please contact your school’s student services for further advice, as different schools have different procedures for validating this exemption.
Language studies are incorporated into students’ elective studies.
Further information on relevant courses is available from the Language Centre: Language and communication studies
School of Science:
Application for the exemption from the obligatory foreign language requirement
Send the filled application to the planning officer of the programme by email. You can find the contact information on Contact page.
Master's thesis 30 cr
Students are required to complete a master's thesis, which is a research assignment with a workload corresponding to 30 credits. The thesis is written on a topic usually related to the student's major and agreed between the student and a professor of the major. The supervisor of the thesis must be a professor in Aalto university and have a relevant expertise in the topic, whereas the thesis advisor(s) can be from the Aalto university, company or another university. Thesis advisor(s) must have at least a master’s degree.
Master’s thesis work includes a seminar or equivalent presentation. The student is also required to write a maturity essay related to the master’s thesis.
The master’s thesis is a public document and cannot be concealed in any part.
Topics of master's theses as well as final theses are approved in the Programme Committee of Life Science Technologies. Approval of topic as well as approval and grading of thesis have to be applied for.
For further information and instructions, see Completing your master's thesis.
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