RESEARCH ENGINEER - OPTIMIZING BACTERIOPHAGE THERAPY IN THE RESPIRATORY SYSTEM VIA HPC (R2) - AI4S

About BSC The Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) is the leading supercomputing center in Spain.
It houses MareNostrum, one of the most powerful supercomputers in Europe, was a founding and hosting member of the former European HPC infrastructure PRACE (Partnership for Advanced Computing in Europe), and is now hosting entity for EuroHPC JU, the Joint Undertaking that leads large-scale investments and HPC provision in Europe.
The mission of BSC is to research, develop and manage information technologies in order to facilitate scientific progress.
BSC combines HPC service provision and R&D into both computer and computational science (life, earth and engineering sciences) under one roof, and currently has over staff from 60 countries.
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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.
Context And Mission CASE department mission is to develop scientific and engineering software to efficiently exploit super-computing capabilities (biomedical, geophysics, atmospheric, energy, social and economic simulations).
The position is for the Physical and Numerical Modelling (PNM) group of this department.
On the one hand, the group engages in both transversal and vertical (applied) activities.
We are primarily responsible for the development and maintenance of the Alya multi-physics and high-performance computing simulation code.
On the other hand, a major focus of our applied research is the biomedical sector.
This includes the cardiovascular and respiratory systems, as well as the cerebra-spinal fluid system, arterial system, women's health, and intervertebral discs.
In recent years, we have developed models and simulations related to the respiratory system, concentrating on drug delivery and pollutants transport.
Our projects have included outdoor simulations for studying virus propagation, drug delivery in the nostrils and small airways, lung airflow for drug delivery and pollutant absorption, particle-mucus interactions for drug absorption, alveoli simulations for bacteria propagation, coupling with intracellular cell signalling to describe infection evolution, and transport in mucus for bacteriophage therapy.
With antimicrobial resistance becoming an expanding global health threat, bacteriophage therapy is (re)gaining interest in the treatment of bacterial infections.
Chronic rhinosinusitis (CRS) is a complex multifactorial inflammatory upper airways disorder, with symptoms such as nasal obstruction, facial pain, and reduced sense of smell, which affects % of the worldwide population.
The antibiotic resistance shown by some of the bacteria involved in this disease, such as Pseudomonas Aeruginosa, is alarming globally.
For this reason, CRS is doomed to become difficult to treat, leading to longer hospital stays, higher medical costs, and increased morbidity and mortality.
Unfortunately, the development of new antibiotics is relatively slow compared to the emergence of resistant strains, which endangers the entering of a post-antibiotic era.
The main goal of the position is to develop a multi-scale, multi-physics computational framework to study the efficacy of phage therapy for the treatment of CRS.
It aims at determining drug delivery parameters and phage combinations for a successful, patient-specific treatment.
As a first step, the candidate will develop models to simulate intranasal administration of phages (single type or multiple types), obtaining their exact location of action (or deposition map) inside the nasal cavity and the paranasal sinuses.
This involves complex phenomena like splashing and spreading when considering liquid carriers or bouncing when considering solid ones interacting with the nasal cavity walls.
Subsequently, the candidate will model phages-bacteria dynamics, including motion, encounters, and interactions, within the mucus layer.
Ultimately, she/he will extract relevant biomarkers (i.
., number of bacteria killed per administration) to unveil the efficacy of the treatment.
Regarding numerical methods, all algorithms will be implemented in Ayla, and simulations will be conducted in a high-performance computing (HPC) environment.
The numerical implementations in Alya must adhere to high HPC standards to ensure efficiency at both parallel and node levels.
This includes solving fluid and solid equations, particle transport, and considering multi-scale interactions from large airways to the mucus layer.
To fully utilise numerical resources at the node level, specific runtime techniques must be employed.
Part of this work will be done in collaboration with the Computer Science department.
Indeed, the proposed workflow is also computationally challenging due to the involvement of multi-physics and multi-scale phenomena across both space and time.
In addition to patient-specific simulations, the candidate will conduct virtual population studies to statistically evaluate treatment efficacy.
This involves running thousands of simulations, which can be computationally expensive.
Machine learning techniques are powerful tools for reducing this cost.
By using a virtual population, the number of required simulations can be significantly reduced by training a model to predict key biomarkers previously identified.
Machine learning techniques will also be considered at all stages of the simulation workflow, primarily as potential surrogates and to support data analysis.
This interdisciplinary research will necessitate close collaboration with clinicians for modelling and analysis, experimentalists for validation purposes, and molecular dynamics groups to develop continuous models from simulations that describe the complex interactions between phages and bacteria.
The funding for these actions/fellowships and contracts comes from the European Union Recovery and Resilience Facility - Next Generation, within the framework of the General Invitation by the public business entity Red.
s to participate in the talent attraction and retention programs within Investment 4 of Component 19 of the Recovery, Transformation, and Resilience Plan.
For more information, please check.
https://www.
sc.
s/join-us/excellence-career-opportunities/ai4s "La financiación de estas actuaciones/becas y contratos, procede del Mecanismo de Recuperación y Resiliencia de la Unión Europea-Next Generation, en el marco de la Invitación General de la entidad pública empresarial Red.
s para participar en los programas de atracción y retención del talento dentro de la Inversión 4 del Componente 19 del Plan de Recuperación, Transformación y Resiliencia.
Para más información.
https://www.
sc.
s/join-us/excellence-career-opportunities/ai4s " Key Duties Development of models to simulate the intranasal administration of phages and obtain deposition maps Modelling of phage-bacteria interactions, including phage motion, encounters with bacteria, and dynamics within the mucus layer Numerical implementation of developed models in Alya following a CI/CD approach Supervision of bachelor, master and PhD students Responsible of documentation, verification and validation Collaboration with medical doctors, experimentalists and molecular dynamics experts Requirements Education PhD in Applied Mathematics, Physics or Engineering Essential Knowledge and Professional Experience Proficiency in Fortran programming language.
Extensive experience in high-performance computing (HPC) and parallelization techniques, including MPI and OpenMP.
Strong background in performance analysis and optimization of computational processes.
Expertise in Computational Fluid Dynamics (CFD) and its applications.
In-depth knowledge of numerical methods and their implementation in scientific computing.
Additional Knowledge and Professional Experience Fluency in English is essential.
Proficiency in Spanish and other European languages would be advantageous.
Proficiency in computational modelling and simulation techniques, particularly in multi-scale and multi-physics frameworks.
Previous experience in leading research projects.
Experience in managing research teams, promoting a collaborative and productive work environment.
Competences Ability to work in a team and in a multi-cultural environment.
Ability to work effectively in an interdisciplinary team environment, collaborating with clinicians, experimentalists, and other researchers.
Ability to supervise and mentor students, fostering their development and ensuring effective project contributions.
Strong written and oral communication skills for preparing reports, scientific papers, and presentations.
Conditions The position will be located at BSC within the CASE Department We offer a full-time contract (37.
h/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 Duration.
4 years Holidays.
23 paid vacation days plus 24th and 31st of December per our collective agreement Salary.
€ Additional Expenses Grant.
Each fellowship will be associated with a grant for additional expenses, such as IT equipment, travel, training, stays, etc.
Starting date.
asap - the incorporation for this vacancy must be before the 16th of December Applications procedure and process All applications must be submitted via the BSC website and contain.
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.
For more information, please follow this link.
Deadline The vacancy will remain open until a suitable candidate has been hired.
Applications will be regularly reviewed and potential candidates will be contacted.
OTM-R principles for selection processes BSC-CNS is committed to the principles of the Code of Conduct for the Recruitment of Researchers of the European Commission and the Open, Transparent and Merit-based Recruitment principles (OTM-R).
This is applied for any potential candidate in all our processes, for example by creating gender-balanced recruitment panels and recognizing career breaks etc.
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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