What we look for in an applicant? |
Applicants to the Life Science Technologies programme are expected to have a high-quality Bachelor's degree, primarily in the field of Engineering, Technology or Science. A suitable degree should contain in total of at least 90 ECTS (90 ECTS corresponds to approximately 1,5 years of full-time studies) credits of studies belonging in at least two of the three following subject categories:
1. computational sciences: algorithms and data structures, artificial intelligence, computer programming, databases, human-computer interaction, machine learning, and data science
2. mathematics: calculus, discrete mathematics, linear algebra, probability, statistics
3. natural sciences: biomedicine, biophysics, biotechnology, chemistry, molecular biology, neuroscience, material science, physics
The content of the applicant’s previous degree(s) is evaluated based on the self-evaluation form and courses available on the official transcript of records. Relevant work experience, professional certificates and/or online courses are judged case-by-case, but they do not, in general, compensate for the university-level studies of the theoretical foundations of the required subjects.
Students selected to the programme may freely choose their major, if they have the pre-requisite knowledge for the mandatory courses comprising the major. Applicants are asked to fill in an online self-evaluation form to assess how well their background matches the knowledge requirements of the preferred majors. The suitability of each applicant for the two most-preferred majors, as indicated in the self-evaluation form, is evaluated during the admission. Students with insufficient background for their preferred majors can be rejected or might be requested to take complementary studies of up to 20 ECTS. The major selection will be finalized as part of the personal study plan in the beginning of studies.
Areas of required and additional knowledge considered as an advantage are defined separately for each major below.
Bioinformatics and Digital Health
Required knowledge:
- computational sciences: algorithms and data structures, databases, programming
- mathematics: linear algebra, calculus, discrete mathematics, probability theory and statistics
Additional knowledge in the following areas is considered an advantage:
- bioinformatics and computational biology
- computational modelling and data analysis
- health technology
- molecular biology and biomedicine
- physics and biophysics
Biomedical Engineering
Required knowledge:
Additional knowledge in some of the following areas is considered an advantage:
- electronics
- computational modelling and data analysis
- neuroscience or medicine
- biomaterials science
- physical chemistry
Biosensing and Bioelectronics
Required knowledge:
Additional knowledge in some of the following areas is considered an advantage:
- electronics
- computational modelling and data analysis
- electrochemistry
- signal processing
- microsystem technologies
- basic programming skills
- chemistry or material sciences
Biosystems and Biomaterials Engineering
Required knowledge:
- mathematics
- physics, chemistry
- molecular and cellular biology
- basic programming skills
Additional knowledge in some of the following areas is considered an advantage:
- gene technology
- synthetic biology
- organic chemistry
- laboratory experience
Complex Systems
Required knowledge:
- computer science: algorithms, data structures, databases, programming
- mathematics: linear algebra, calculus, discrete mathematics, probability and statistics
Additional knowledge in the following areas is considered an advantage:
- statistical physics
- computational modelling and data analysis
- information theory
- machine learning
- network theory
- nonlinear dynamics
Human Neuroscience and Technology
Required knowledge:
- mathematics: linear algebra, calculus and statistics
- physics
- basic programming skills
Additional knowledge in some of the following areas is considered an advantage:
- computational modelling and data analysis
- machine learning
- signal processing
- psychology or sociology
- biology or medicine
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