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We are particularly interested for this role in the strengths and lived experiences of women and underrepresented groups to help us avoid perpetuating biases and oversights in science and IT research. In instances of equal merit, the incorporation of the under-represented sex will be favoured.
We promote Equity, Diversity and Inclusion, fostering an environment where each and every one of us is appreciated for who we are, regardless of our differences.
If you consider that you do not meet all the requirements, we encourage you to continue applying for the job offer. We value diversity of experiences and skills, and you could bring unique perspectives to our team.
The Computational Biology group, led by ICREA professor Alfonso Valencia, is looking for a Research Engineer (RE2) to work in the framework of a European project: COMMUTE: Comorbidity mechanisms utilized in health care. This project is divided into two main sections: a hypotheses-free, data-driven approach is building on available big data and the application of cutting edge AI/ML technologies to answer the question, whether infection by SARS-CoV-2 causes effects that result in a higher risk for the development of neurodegenerative diseases at population-level. Complementary to that, a hypothesis-driven, knowledge-based approach leverages the substantial knowledge in the scientific community working on neurodegenerative diseases on the putative comorbidity mechanisms linking COVID and neurodegeneration.
The selected candidate will be responsible for applying different machine learning and artificial intelligence approaches to predict the development of neurodegenerative diseases analyzing data coming from electronic health records and several cohorts. The selected candidate will work in a highly sophisticated High-Performance Computing (HPC) environment, have access to state-of-the-art systems and computational infrastructures, and establish collaborations with international and local experts in different areas of biomedical research.

- Train and evaluate different AI/ML model architectures for the prediction of the development of neurodegenerative diseases.
- Model interpretation using SHAP.
- Education
- Degree in engineering or science with sufficient knowledge and interest in biology.
- MSc in data science, machine learning or similar.
- Essential Knowledge and Professional Experience
- At least 3 years of experience using machine learning and artificial intelligence methodologies, and knowledge of epidemiology.
- Additional Knowledge and Professional Experience
- Knowledge in statistics.
- Knowledge and experience in AI methodologies.
- Programming: Python and R.
- Knowledge and experience in data science methodologies: Data pre/post-processing (feature selection, dimensionality reduction, plotting and visualization). Time-series analysis. Deep learning theory and frameworks (PyTorch, Keras, TensorFlow). High-performance computing (HPC). Fundamentals of linear algebra. Bayesian inference.
- Knowledge and experience in life sciences research.
- Fluency in spoken and written English.
- Competences
- Ability to work both independently and within a team.
- High motivation and scientific interest.
- The position will be located at BSC within the Life Sciences Department
- We offer a full-time contract (37.5h/week), a good working environment, a highly stimulating environment with state-of-the-art infrastructure, flexible working hours, extensive training plan, restaurant tickets, private health insurance, support to the relocation procedures
- Duration: Open-ended contract due to technical and scientific activities linked to the project and budget duration
- Holidays: 23 paid vacation days plus 24th and 31st of December per our collective agreement
- Salary: we offer a competitive salary commensurate with the qualifications and experience of the candidate and according to the cost of living in Barcelona
- Starting date: asap
- A full CV in English including contact details
- A cover/motivation letter with a statement of interest in English, clearly specifying for which specific area and topics the applicant wishes to be considered. Additionally, two references for further contacts must be included. Applications without this document will not be considered.
Development of the recruitment process
The selection will be carried out through a competitive examination system ("Concurso-Oposición"). The recruitment process consists of two phases:
- Curriculum Analysis: Evaluation of previous experience and/or scientific history, degree, training, and other professional information relevant to the position. - 40 points
- Interview phase: The highest-rated candidates at the curriculum level will be invited to the interview phase, conducted by the corresponding department and Human Resources. In this phase, technical competencies, knowledge, skills, and professional experience related to the position, as well as the required personal competencies, will be evaluated. - 60 points. A minimum of 30 points out of 60 must be obtained to be eligible for the position.
The recruitment panel will be composed of at least three people, ensuring at least 25% representation of women.
In accordance with OTM-R principles, a gender-balanced recruitment panel is formed for each vacancy at the beginning of the process. After reviewing the content of the applications, the panel will begin the interviews, with at least one technical and one administrative interview. At a minimum, a personality questionnaire as well as a technical exercise will be conducted during the process.
The panel will make a final decision, and all individuals who participated in the interview phase will receive feedback with details on the acceptance or rejection of their profile.
At BSC, we seek continuous improvement in our recruitment processes. For any suggestions or comments/complaints about our recruitment processes, please contact recruitment [at] bsc [dot] es.
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BSC-CNS is an equal opportunity employer committed to diversity and inclusion. We are pleased to consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or any other basis protected by applicable state or local law.
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