Bioinformatics aims at gaining a better and preferentially more quantitative molecular understanding of cellular processes by integrating and modeling high throughput molecular data (omics data). This requires the use and development of state of the art techniques for storing, retrieving, organising, analysing and interpreting biological data.
Competence field 1: Competence in one or more scientific disciplines
Having advanced knowledge of mathematics, informatics, machine learning and statistical techniques and their application within bioinformatics and systems biology.
Having knowledge of experimental techniques for the generation of 'omics’ data.
Understanding the specificities of ‘ bioinformatics’ and ‘systems biology’ in relation to its composing subdomains i.e. having insights in the interdisciplinary character of the research domain.
Application of basic statistical, computer science and other data analysis techniques to solve well-delineated problems (skill).
Having an overview of the most important methods in computational biology.
Having insight in the way bioinformatics evolves (fastly evolving domain).
Knowledge of software-engineering techniques and advanced programming skills.
Advanced knowledge of data structures and algorithms for the application of well delineated problems.
Broad knowledge of the bioinformatics application domain.
Broad knowledge of the genetics and molecular biology.
Competence field 2: Scientific competence
Implement previously described models and methods to solve a bioinformatics problem.
Design novel analysis tools and methods to solve a new bioinformatics problem.
Design the proper simulation studies to evaluate state-of-the-art methods.
Recognize a biological problem and determine the proper method to solve it with a bioinformatics approach.
Interpret the results of a model or simulation from a computational or biological point of view.
Competence field 3: Intellectual competence
Define a complex systems biology problem and subdivide this in subproblems.
Choosing the most appropriate principles to solve each of the subproblems, if needed in collaboration with experts in each of the subdomains (informatics, statistics).
Make a well-educated choice between the theoretical ly most elegant and most prag matic method s and estimate the effects of appr oximations on the final results.
Showing an active attitude towards life- long learning.
Competence field 4: Competence in collaboration and communication
Communicate in English in the own domain.
Work in a project driven way: formulating goals and focused reporting , taking into account the end goals, the development trajectory and the background of the vocational field (bioinformaticians, biologists, clinicians, statisticians, computer scientists).
Function as a member of a team in a multidisciplinary environment and as starting manager.
Oral, written and graphical reporting on a scientific topic and placing it in a broader framework.
Competence field 5: Competence in social responsibility
Having an insight in the ethical questions raised by the fastly evolving domain of bioinformatics and systems biology ( persona lized medicine, successful aging , sustainable agriculture, synthetic biology , … ).
Being aware of the social and bioethical discussions that relate to the data and the analysis results.
Taking into account the running ethical norms in scientific research (e.g. dealing with patient data).
Competence field 6: Professional competences
Gaining insight into the complexity of the problem with quantitative methods.
Formalize a biological problem taking into account the properties of the data and the assumptions of the method.
Extract useful infor mation from abun dant, incomplete and contradictory data.
Test the results of complex calculations and approximations.
Have attention for running times, performance, memory requirements and user-friendliness of the algorithms and bioinformatics tools.
Have attention for aspects such as reliability and confidence during storage and transmissions of big data.
Having an insight in the understanding and role of entrepreneurship.
Show attitude of perseverance, innovation and added value creation.
Plan and execute in an independent and results-driven way an engineering project at the level of a beginning professional.