Within Piano Nazionale Complementare al PNRR (PNC) initiative FBK is looking for a researcher in the field of Data Science for Digital Health

Funding reference PNC... - D3-4- Health - Digital Driven Diagnostics, prognostics and therapeutics for sustainable Health care.
AFFILIATO SPOKE 2 CUP: B53C...
Avviso MUR nr. 931 di data 6/6/2022 PNC: Piano nazionale per gli investimenti complementari al PNRR - Avviso per la concessione di finanziamenti destinati ad iniziative di ricerca per tecnologie e percorsi innovativi in ambito sanitario e assistenziale.
Iniziativa a valere sull’ Intervento, a titolarità del Ministero dell’ Università e della ricerca, di cui all’art. 1, comma 2, lett. i) del decreto-legge 6 maggio 2021, n. 59, convertito, con modificazioni, dalla legge 1° luglio 2021, n. 101, di approvazione del Piano nazionale per gli investimenti complementari al Piano nazionale di ripresa e resilienza” Responsabile del Procedimento: Alessandro Dalla Torre Within Piano Nazionale Complementare al PNRR (PNC) initiative FBK is looking for a researcher in the field of Data Science for Digital Health FBK is a research institution devoted to excellence in research in numerous disciplines and designated to the role of keeping the Autonomous Province of Trento in the mainstream of European and international research.
Each research area is assigned to a specific research Center, of which there are eleven totals.
Information regarding the research Centers, their activities and production are available at .
The Digital Health & Wellbeing Center is one of the Centers of the Bruno Kessler Foundation (FBK).
The activities of the Center for Digital Health & Wellbeing mainly concern scientific research of excellence in the field of Computer Science and AI techniques and methodologies for health and healthcare, as well as social and technological innovation for a relevant impact on both the local community and nationally and internationally.
Workplace Description The Data Science for Health (DSH) Research Unit focuses on the design, the development, and the implementation of predictive models for the life sciences.
In particular, statistical machine learning and deep learning algorithms are applied to integrated health data such as Electronic Health Records (EHR), different levels of -omics data and bioimages of diverse nature, from CT, PET, MRI scan to Digital Pathology WS Is.
We aim to create novel mathematical methods and ICT platforms connecting physiopathological patterns of disease with high dimensional data now available for Functional Genomics (e. g DNA microarrays, SN Ps, proteomics, Deep Sequencing), with clinical and imaging data and geodatabases of environmental factors and socio-demographic data.
Background information and description of research projects This position is offered within Piano Nazionale Complementare al PNRR (PNC) initiative, within FBK has proposed the project " Digital Driven Diagnostics, prognostics and therapeutics for sustainable Health care" (D3-4-HEALTH).
The mission of the project is to deliver highly technological solutions that will impact the management of 5 reference diseases.
D3-4-HEALTH will fundamentally transform and advance current methodologies for the diagnosis, monitoring and therapy of the reference diseases facilitating the deployment of precision medicine approaches through the development of the digital and biological twins, for the benefit of patients.
D3-4-HEALTH will escalate and deploy innovative and non-invasive technologies and solutions, based on the analysis of digital and digitalized healthcare data.
Job Description FBK is looking for candidates to cover the position of Researcher with a dynamic, highly motivated, researcher in the field of Data Analytics for Digital Health for the DSH Unit of Digital Health and Wellbeing Center.
The role requires good programming skills, together with a good knowledge of the basics of machine learning and data science, with a particular emphasis on mastering deep neural networks, both theoretically and practically, aimed at the construction of end-to-end pipelines to be run on different computational facilities.
Within the Project, the expected activities will be: support the project team in the design, optimization, implementation, running and validation of predictive machine learning models, both shallow and deep, on local GPU, distributed clusters, and cloud facilities.
Support the project team in the software development and the maintenance of the learning platform and infrastructure.
Support the project team in the results dissemination activities, at different levels (e. g. , website maintenance, paper writing).
Help supervising B.
Sc, M.
Sc. and Ph.
D. students.
Help managing the project. Job requirements The Ideal Candidate should have: PhD Degree or equivalent 3 years post-master experience in areas related to Computational Life Sciences; Solid knowledge of the basic concepts of machine learning and deep learning, including mastering all steps of the construction of a modeling pipeline, from design and development to implementation, optimization and testing.
Excellent knowledge of the Python language and its data science libraries (e. g. , scikit-learn), including Deep Learning libraries (e. g. , Py Torch) Solid knowledge of the Digital Health domain and the related data, e. g.
EHR, omics, biomedical imaging, Good track record (major conferences and top-ranked journals) in publishing in one or more of the following fields: data science, machine learning, applied mathematics, statistics; Basic knowledge of the R programming language; Good knowledge of usage and administration of HPC cluster solutions; Language assessment according to the Common European Framework of Reference for Languages (CEFR): level of knowledge required.
Knowledge of English will be verified during the interview on a technical or scientific topic and must be equal to or exceed level B2.
Definitions of levels can be found at the following link ; Experience in working for research projects; Teamwork approach, good communication and relational skills; Time management, planning, and development skills; Accuracy, flexibility, proactivity, and goal orientation. Employment Type of contract: fixed-term contract Working hours: full-time (38 h per week) Start date: November 2024 End date: until 30th November 2026.
The duration of the contract is limited to the completion of activities pertinent to the specific research project and in any case to the research project itself. Contract type: CCPL Research Foundation Personnel ( Personale/ Contrattazione-collettiva/ Rinnovo-CCPL-delle- Fondazioni) for a third level Researcher, the current gross annual remuneration is Euro 41. 475. Workplace: Povo, Trento ( Italy) Benefits: flexi-time, company subsidized cafeteria or meal vouchers, internal car park, welcome office support for visa formalities and for research in accommodation, supplementary pension ( Resaver, Laborfonds) and health fund ( Sanifonds), family-work balance, free training courses, support on bank account opening, discount on public transport, sport, language course fees, counseling and psychological support service.
More info at Application Interested candidates are requested to submit their application by completing the online form ().
Please make sure that your application contains the following attachments (in pdf format): detailed CV; cover letter indicating why the candidate is suitable for this position. Application deadline: 10th November 2024 Selection process and assessment criteria The Evaluating Committee will be appointed by the HR Director at the end of the application deadline. The recruiting process will be handled in accordance with the “ Gender and generational equal opportunities, as well as the employment inclusion of people with disabilities in public contracts financed with the resources of the PNRR and PNC” guidelines and with the Foundation's Gender Equality Plan.
The Committee may compile the short-list of the candidates admitted to the interview, remotely or in presence.
The short-list shall be compiled based on the requirements set out in the call (contained in the requirements of the job description), with the support of the screening of C Vs and any other required documents. Candidates with a minimum score will be admitted to the interview phase.
Shortlisted candidates must do at least one interview with the Committee. In case of specific need, the Selection Committee can also meet remotely, by teleconference or videoconference, provided that all members can be identified and that they are able to follow and intervene in the discussion, as well as to receive, transmit and view documents.
During the evaluation step, evaluation support tools such as tests or questionnaires may be used.
Furthermore, group tests and/or practical tests may be administered.
Evaluation criteria The recruitment process will be based on the total points obtained by the evaluation of the qualifications/ expertise and the evaluation of the interview. A maximum of 40 points will be allocated for qualifications and expertise that the candidate expresses on the resume, while the interview will be worth up to 60 points.
The interview scores will be assigned to candidates by the Commission according to the following criteria: the presentation of their personal research profile; the knowledge about the scientific domain, the experience in working for research projects and the language skills.
Only candidates obtaining at least 25 points in the evaluation of the qualifications and the expertise will be admitted for the interview.
The interview is considered as “passed” if the applicant obtains at least 45 points.
The final score will be used to generate the final suitability list for each job position .
Results of the selection process All candidates will be notified via email once the selection process has been completed.
The suitability list may be used to fill the position in case the successful candidate doesn’t accept the job offer. At the website in the " Selection results" section, will be published the details of the selection process and the final results. Diversity & Inclusion policy FBK actively seeks diversity and promotes inclusion in the workplace.
The main aims of the FBK Diversity & Inclusion policy are to: promote gender equality across the research domains and on all levels by encouraging qualified female candidates to apply for job positions and by implementing specific improvements and measures as stated in the Gender Equality Plan (GEP) foster young talents development by offering opportunities to grow become a disability-inclusive organization by encouraging applications from candidates with a disability ( Law 68/99).
We provide special assistance to applicants during the recruitment procedure and reasonable arrangements for disabled staff promote a healthy work-life balance by offering a package of flexible working arrangements and facilities (telework, individual working time, parental leave, etc). The FBK operates in compliance with current legislation concerning fixed-term contracts.
Candidates with disabilities are invited to state whether they belong to the categories referred to in Law 68/99, and to indicate this in the curriculum vita sent in application for recruitment.
Processing of personal data Pursuant to art. 13 of EU Regulation No. 2016/679 (GDPR), we inform you that your personal data shall be processed for the management of the selection process and of the obligations connected to it, through manual, electronic and informatic means and will be guaranteed within privacy and security standards as indicated in the full privacy policy.
In order to ensure and respect the principles of publicity, transparency and impartiality, the name of the successful candidate and the names of suitable candidates will be published on the FBK website following acceptance of the position.
For further information, please contact the Human Resources Services at jobs@fbk. eu.