Fmup | 1 (One) Research Studentship | Project ''Medisynth'' (Ref. Compete2030-Feder-03069100) | Ref.

Organisation/Company Faculdade de Medicina da Universidade do Porto Department HR - Human Resources Research Field Medical sciences » Health sciences Researcher Profile First Stage Researcher (R1) Positions Bachelor Positions Application Deadline 13 Nov ***:59 (Europe/Lisbon) Country Portugal Type of Contract Permanent Job Status Full-time Hours Per Week 35 Is the job funded through the EU Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No
Offer Description Call for the award of ONE (1) Research Grant (BI), within the scope of the project "MediSynth – Synthetic Data for Healthcare" (Ref. COMPETE-FEDER-**), CO-FINANCED BY THE EUROPEAN REGIONAL DEVELOPMENT FUND (ERDF), THROUGH THE THEMATIC PROGRAM FOR INNOVATION AND DIGITAL TRANSITION (COMPETE **), WITHIN THE SCOPE OF PORTUGAL , ONGOING AT THE FACULTY OF MEDICINE OF THE UNIVERSITY OF PORTO (BOLS-FMUP-*)
A call is open for the award of one (1) research grant (BI) for the holder of a bachelor's degree enrolled in a course that does not confer an academic degree integrated in the educational project of a higher education institution, developed in association or cooperation with one or more R&D units, to carry out R&D activities, within the scope of the project
"MediSynth – Synthetic Data for Healthcare" (Ref. COMPETE-FEDER-**) , co-financed by the European Regional Development Fund (ERDF), through the Thematic Program for Innovation and Digital Transition (COMPETE **), within the scope of Portugal **, ongoing at the Department of Community Medicine, Health Information and Decision (MEDCIDS) of the Faculty of Medicine of the University of Porto, under the following conditions:
1. Scientific Area(s):
Digital Health, Health Data Science and related areas.
2. Admission requirements:
All adult persons with the following requirements, proven by documents, may apply for this competition:
To be enrolled, or to become enrolled by the date of contracting, in a course that does not confer an academic degree integrated in the educational project of a higher education institution, developed in association or cooperation with one or more R&D units, to carry out R&D activities, and the respective proof must be submitted by the date of contracting.
3. Conditions of preference, proven by documents:
Additional training, experience or knowledge in digital health, data science, artificial intelligence applied to health, medical informatics, clinical research or related areas;
Experience or knowledge in data analysis, management, curation or annotation, research methodologies, data evaluation or use of data analysis tools;
Experience or expertise in at least one of the following areas: synthetic data, generative artificial intelligence, language models, natural language processing, multimodal data, data quality, privacy, bias/fairness, or evaluation of digital health solutions;
Participation in projects, research activities, literature review, production of reports, communications or other scientific or technical-scientific results;
English language competence appropriate to the reading and production of scientific documentation and to participation in activities of an international consortium, proven by certificate, training, academic/professional experience, written production or participation in scientific activities in English.
4. Work plan:
4.1. MediSynth aims to develop and validate methods and tools for the generation and evaluation of multimodal synthetic data in health, including text, audio and other clinical modalities, with particular attention to quality, usefulness, privacy, bias, interpretability and clinical relevance. The grant holder will carry out, in a complementary way, the following activities:
Support the definition and refinement of clinical use cases and the systematization of medical, technical, ethical and evaluation requirements;
Conduct research, review, and synthesis of scientific literature on synthetic data generation and evaluation in health, generative artificial intelligence, multimodal data, and validation methodologies;
Support the organization, curatorship, annotation, structuring and analysis of data and results necessary for the research and validation activities of the project;
Collaborate in the application and analysis of metrics of quality, usefulness, realism, privacy, bias and fairness of synthetic data and in the comparison of approaches and results;
Support the preparation and execution of clinical and/or user validation activities, including organization of instruments, collection and systematization of results and feedback;
Support the preparation of reports, deliverables, publications, scientific communications and other dissemination activities of the project;
Other related tasks assigned to the grant holder within the scope of