ORGANISATION/COMPANYUniversitat Pompeu Fabra - ETIC
RESEARCH FIELDEngineering › Biomedical engineering
RESEARCHER PROFILEFirst Stage Researcher (R1)
APPLICATION DEADLINE26/04/2021 21:00 - Europe/Athens
LOCATIONSpain › Barcelona
TYPE OF CONTRACTTemporary
HOURS PER WEEK37,5
OFFER STARTING DATE01/09/2021
EU RESEARCH FRAMEWORK PROGRAMMEH2020
Two PhD positions are available at the BCN MedTech Research Unit (https://www.upf.edu/web/bcn-medtech/), Department of Information & Communication Technologies (DTIC) of the Universitat Pompeu Fabra (UPF), Barcelona, Spain, as part of the EU Horizon 2020 SimCardioTest project (2021-2024; SC1-DTH-06-2020, Grant agreement No. 101016496), ideally starting September 2021 (earlier start should also be possible). SimCardioTest is a recently granted European project involving a large consortium of clinical, industrial and academic partners (see Figure 1), with the main goal of developing a standardised and secure cloud-based platform where in-silico trials run seamlessly. UPF is the leader of the cardiac use case related to left atrial appendage occluder (LAAO) devices, where we will demonstrate the platform effectiveness, along with the required verification and validation processes and certification support to optimise LAAO device settings.
PhD project 1 (PhD1): Computational models for the prediction of device-related thrombus in LAAO devices
In collaboration with other partners of the consortium (mainly Boston Scientific, IHU Bordeaux and Simula), as well as several junior/senior researchers at UPF, PhD1 will work on the development of computational models to simulate the thrombus formation process after LAAO device implantation, using cascade and agent-based models, to couple with computational fluid dynamics (CFD) simulations. Fast fluid simulations with GPU-based solvers or deep-learning will also be investigated. A portfolio of realistic CAD models of LAAO devices will be created. Additionally, the effects of (anti-coagulants, anti-platelets) drug therapies will also be modelled. Virtual contrast agents and porous flow modelling through device frames after implantation will also be investigated to characterise acute response. PhD1 will also contribute to the generation of a large virtual population of in-silico fluid simulations and the development of quantitative metrics to characterise flow dynamics. These tasks will leverage on the past research by UPF members on LAAO-based fluid simulations (e.g., Aguado et al., 2019, https://doi.org/10.3389/fphys.2019.00237; Mill et al., 2020, https://doi:org/10.1016/j.cjca.2019.12.036) and agent-based models of atherosclerosis (Olivares et al., 2017, https://doi:org/10.1093/bioinformatics/btw551). PhD1 will be directly supervised by Pr Oscar Camara and Dr Jérôme Noailly from BCN MedTech at UPF.
PhD2 project (PhD2): Machine learning for the optimisation of device and drug therapy treatment in patients with atrial fibrillation
In collaboration with other partners of the consortium (mainly Inria, IHU Bordeaux, Boston Scientific), and junior/senior researchers at UPF, PhD2 will work on the development of machine learning tools to predict the optimal LAAO device settings for each individual patient. Morphological descriptors of the LA and LAA will be extracted to characterise their geometry, which will be combined with in-silico indices provided by fluid simulations run with multiple combinations of LAAO device settings. Unsupervised clustering methods such as the ones based on Multiple Kernel Learning, in which UPF has long-standing experience (e.g., Sanchez-Martinez et al., 2018, https://doi.org/10.1161/CIRCIMAGING.117.007138) will be explored to identify patterns among the data for a better understanding of the most relevant biomarkers predicting good prognosis. The use of deep learning techniques as surrogates of fluid simulations, based on our ongoing work (e.g., Morales et al., 2019, https://doi.org/10.1007/978-3-030-39074-7_17; Acebes et al., 2020, https://doi.org/10.1007/978-3-030-68107-4_4). PhD2 will be directly supervised by Pr Oscar Camara and Pr Gemma Piella from BCN MedTech at UPF.
The selection committee uses a number of indicators to evaluate the applicant’s preparedness, motivation and potential.
1st phase, remote pre-selection:
The Scientific, Technological & Academic excellence will be considered at first, based on:
• Quality of the CV, in general
• Any demonstrated research experience, particularly if supported by evidence such as scientific publications, patents, participation in scientific congresses, …
• Undergraduate performance: overall, with a special focus on relevant field-specific courses
• Any demonstrated previous recognitions (grants, awards, …)
• Reference letters provided by professors and senior scientists: Two refence letters are expected. Referees are asked to address analytical capabilities, technical proficiency, ability to work independently and motivation/commitment.
• Statement of purpose: past research experience, motivation for applying to this particular PhD project, academic fit, contribution of the project to the candidate’s future careers plans, ...
• Additional relevant skills (field-specific): demonstrated, e.g., through previous projects, and or through previous participation in scientific contests, trainings, ...
2nd phase, interview(s):
Should the candidate be preselected at phase 1, a second phase will consist in at least one interview through which the motivation, the proactive behaviour, the capacity to work collaboratively, the organizational skills, the communication skills and the capacity to engage in a scientific discussion and manage problems, will be assessed, among other aspects. The final decision will be the result of a consensus of an evaluation committee that will take into account the results of both recruitment phases 1 and 2.
The candidate will be informed of the section results by email. Application process All the documents that prove the eligibility of the candidate and should be provided.
As for the selection process candidates are expected to provide at least the following documents:
• A brief introduction letter (no more than one A4 page) that summarizes the documents and the nature of the information provided for the selection
• A full CV
• The two requested reference letters
• The letter of purpose (no more than two A4 pages)
All documents must be sent by email to Pr Oscar Camara .
Deadline: 26th April 2021
We are looking for highly motivated young researchers with a MSc degree (or equivalent) in Biomedical Engineering, Data Science, Physics, Mechanical Engineering, Applied Mathematics, Computational Science, or related disciplines, willing to study and do research at the leading edge of biomedical engineering.
Experience in computer sciences and having proven programming skills would be of importance. High motivation is the only essential pre-requisite; our top-quality research standards demand hard work, which only strong motivation and commitment can ensure. Nevertheless, candidates already familiar with the following methodologies would ensure a faster start of the project: PhD1: Computational Fluid Simulations for biomedical applications, Ansys, Paraview, mesh manipulation software (e.g., MeshMixer, Meshlab, GMSH, Paraview, etc.), agent-based models. PhD2: Supervised and unsupervised machine learning algorithms, computational models.
Candidates must have excellent teamwork and communication skills and be enthusiastic about collaborating with a diverse range of international partners. We expect them to be fluent in oral and, particularly, writing English, as it will be the language used to interrelate with the different partners. Interest in clinical translation is essential since meetings with clinicians will regularly take place.
Female applicants are explicitly encouraged to apply and will be treated preferentially whenever they are equally qualified as other male candidates.
EURAXESS offer ID: 620438
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