DTIC-CAC-2025-48 - Tècnic/a especialista de suport/ Specialist support technician: Research assistant position on predicting thrombogenic risk

Description

Línia de recerca/ Line of research: Development of artificial intelligence algorithms and computational models for supporting clinical decisions in cardiovascular conditions. 

Sensing in Biology and Biomedicine (Physense)

• Camp principal d’investigació/ Main research field: Biomedicina/ Biomedicine

Fons De FinançAment/ Financing Fund:

• PID2022-143239OB-I00 - Generating synthetic data, knowledge, and clinical recommendations with artificial intelligence to improve left atrial appendage therapies. Ministerio Español de Ciencia e Innovación. O. Camara. (2023-2027)

Descripció del projecte/ Project description: The left atrial appendage (LAA) remains an enigmatic feature in the human anatomy, with numerous unknowns regarding its structure and function. Despite ongoing research, it is still uncertain which patients with atrial fibrillation (AF) possess a higher risk of developing thrombi. A granted national project, GENERALITAAT (follow-up of the ones funding our research during the last 7 years), is ushering in a new era for interventional cardiology and LAA therapies, leveraging the most cutting-edge computational technologies such as generative artificial intelligence (AI) algorithms, natural language processing, visual analytics, and in-silico simulations in a clinical setting. Work we conducted in previous projects enabled the joint analysis of morphological and in-silico indices of haemodynamics with classical statistics. Additionally, fluid simulations have allowed the derivation of in-silico biomarkers that can predict the risk of device-related thrombus after LAAO implantation. Furthermore, we have developed dimensionality-reduced latent spaces to identify the parameters most closely associated with stroke events. In GENERALITAAT, we will integrate all available data from each patient, in order to achieve more robust estimations of thrombogenic risk. Furthermore, we will focus on the interpretability of the AI-based results, following recent studies we have published. This step will be inextricably linked with the visual analytics system to be developed. Furthermore, beyond the data already available from the clinical partners in GENERALITAAT, we shall seek access to the UK Biobank dataset, for the specific analysis of atrial fibrillation and stroke patients, as well as using other datasets from a new EU project, GEMINI.

Funció a desenvolupar/ Tasks to be performed:

• Morphological characterization of left atrial anatomies and derivation of multimodal indices on stroke databases
• Use of unsupervised machine learning techniques for phenotyping of stroke patients and estimation of thrombogenic risk
• Automatic estimation of optimal left atrial appendage occluder settings for each patient.

The duration of these tasks is estimated over a period of 5 months.

Tipus de contractació/ Type of contract: Indefinida segons previst a l'article 23 bis de la Llei 17/2022, de 5 de setembre, per la qual es modifica la Llei 14/2011, d'1 de juny, de la  ciència, la tecnologia i la innovació./ Indefinite according to article 23 bis of the “Llei 17/2022, de 5 de setembre, per la qual es modifica la Llei 14/2011, d'1 de juny, de la  ciència, la tecnologia i la innovació”.

Requisits/ Requirements:

• Bachelor degree in Biomedical Engineering. 

The expected start date is November 10th 2025, the job is in Barcelona. 

Criteris de selecció/ Selection criteria: La selecció dels candidats es farà mitjançant valoració del currículum i, si escau, amb la realització d’una prova i/o entrevista. La valoració serà la següent:/ The selection of the candidates will be made through evaluation of the curriculum and, where appropriate, with the carrying out a test and/or interview. Valuation will be as follows:

1- Formació Acadèmica/ Academic Training (0-20 punts/ points).

• BSc and MSc in Biomedical Engineering

2- Altra formació i experiència professional, adequació al perfil proposat/ Other professional training and experience, adequacy to the proposed profile (0-60 punts/ points):

• Experience with unsupervised machine learning techniques in healthcare.
• Experience working with multimodal large databases with patient data.
• International stays in research labs

3- Altres mèrits/ Other merits (0-20 punts/ points):

• Fluent written and oral communication skills in English

• Teamwork Skills

• Solid publication record and participation in international conferences

The minimum score to pass the selection process is 85 points. The candidate with the highest score in the selection process will be offered the job.

• Retribució prevista aproximada/ Planned remuneration approx: 24.579,66 € brut per any/ gross per year
• Grup i complement/ Group and complement: Group 3 + Level u
• Dedicació i jornada/ Dedication and working hours: Part time (30h/week).

Application Instructions

Tractament de dades de caràcter personal/ Processing of personal data:

La participació en aquest procés de selecció comporta l’acceptació de les condicions del tractament de dades personals que es detallen a https://rat.upf.edu/ca/ll040

The participation in this selection process implies the acceptance of the conditions for the processing of personal data as detailed in https://rat.upf.edu/ca/ll040

Equal Employment Opportunity Statement

UPF promotes a diverse and inclusive environment and welcomes applicants regardless of age, disability, gender, nationality, race, religion or sexual orientation.