Data Science Technician for Machine Learning Based Disease Prediction from Large-Scale Multi-Source...

As part of the EarlyCause H2020 project (https://earlycause.eu/ ), we offer an exciting data science technician position at the Universitat de Barcelona and its Artificial Intelligence in Medicine Lab (BCN-AIM). The selected technician will develop algorithms and tools for predictive modelling of individual-specific health and disease trajectories using multi-factorial approaches, integrating biological, environmental and clinical predictors. This position offers the opportunity to take part in the emerging fields of both biomedical artificial intelligence and the human exposome.

We are thus seeking candidates with a BSc degree and expertise in an area pertinent to the project and experience in machine learning, biomedical informatics, bioinformatics, advanced statistics, digital health and/or programming.

Candidates must have excellent teamwork and communication skills, be enthusiastic about their research, and have creative problem-solving skills. The successful candidate is expected to be able to organise their work with minimal supervision, prioritise tasks to meet project deadlines, and represent the lab at meetings. Due to continual collaboration with EarlyCause consortium partners from across Europe, advanced oral and writing English knowledge are required.

The candidate with join the Artificial Intelligence in Medicine Lab (www.bcn-aim.org ), which is an integral part of the University of Barcelona’s Faculty of Mathematics and Computer Science. It is a young and dynamic research lab, highly active in international projects, and composed of 18 enthusiastic academics, researchers, students and research managers, with expertise in data science, machine/deep learning, biomedical informatics, biomedical ethics, and health-related applications.

The EarlyCause H2020 project will leverage a unique collection of birth cohorts, longitudinal data and experimental models to identify causative mechanisms linking early life adversity (in children and pregnant women) to multi-morbidity development. Concretely, the project will focus on depression and two of its main physical comorbidities, namely coronary heart disease and diabetes. The consortium will disentangle the complex biological contributions from four key interconnected domains linked to early life stress (ELS), namely epigenetics, inflammation, neuroendocrine system, and microbiome. Furthermore, modifying effects of environmental factors such as sex/gender, socioeconomics, lifestyle and behaviour will be quantified, thus uncovering potential intervention targets that may reverse the causative mechanisms and reduce the impact of ELS on multi-morbidity development in high-risk individuals.

To achieve the goals of the project, this highly multi-disciplinary and experienced consortium will combine state-of-the-art and novel approaches from basic, pre-clinical and clinical research, including advanced statistical and mathematical methods, animal models of prenatal and postnatal stress, cellular models in various tissues, and integrative bioinformatics and machine learning methods. The consortium members will also enable access and exploitation of the largest set of European cohorts, comprising rich information on early stressors, biological and omics data, as well as depressive, cardiovascular and metabolic clinical outcomes.

The University of Barcelona (UB), founded in 1450, is one of the oldest universities in Spain. It comprises a student body of 84,370 and 4,548 research staff members. With 73 undergraduate programs, 273 graduate programs and 48 doctorate programs, UB is the largest university in Barcelona and Catalonia. The UB is ranked the first Spanish university according to several rankings (QS World University Rankings 2020, ARWU/Shanghai Ranking 2020). It is particularly interested in fostering international relations and, for many years, has managed an average of 150 European projects per year. Since January 2010, the University of Barcelona is part of the prestigious League of European Universities Research (LERU).

Acronym

EarlyCause

FBG project number

402158

Project title

Causative mechanisms and integrative models linking early-life-stress to psycho-cardio-metabolic multi-morbidity

IP

Karim Lekadir

Department

Departament de Matemàtiques i Informàtica

Gross salary per year

€20,000

Required documents

Motivation Letter, Curriculum Vitae

Send your application to:

email

[email protected]

Name

Xènia Puig

email subject

Application – EarlyCause data science technician