Inesc Tec Is Accepting Applications To Award 1 Research Grant For Msc Holders

Organisation/Company INESC TEC Research Field Computer science Engineering » Electrical engineering Researcher Profile First Stage Researcher (R1) Application Deadline 18 Nov ***:59 (UTC) Country Portugal Type of Contract Temporary Job Status Full-time Hours Per Week 36 Offer Starting Date 3 Dec Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number AE* Is the Job related to staff position within a Research Infrastructure? No
CALL FOR GRANT APPLICATIONS (AE*) INESC TEC is now accepting grant applications to award 1 Research Grant (BI) within the scope of the project CELLECTED2, with reference CI26-2U322, funded by"la Caixa" Foundation through the CaixaImpulse Programme
1. GRANT DESCRIPTION Type of grant: Research Grant (BI)
General scientific area: COMPUTER SCIENCE,ENGINEERING
Scientific subarea: Electrical engineering
Grant duration: 12 months, starting on ** , with the possibility of being renewed until the end of the project.
Workplace: INESC TEC, Porto, Portugal
Maintenance stipend: *, according to the table of monthly maintenance stipend for FCT grants ( ), paid via bank transfer. Grant holders may be awarded potential supplements, according to a quarterly evaluation process (Articles 19, 21 and 22 of the Regulations for Grants of INESC TEC and Annex II), up to a maximum limit of 50 of the monthly maintenance stipend.
The grant holder will benefit from health insurance, supported by INESC TEC.
2. OBJECTIVES: Cancer care is becoming increasingly personalized, yet it still often relies on invasive tests. Bladder; cancer, one of the most expensive cancers to manage, has recurrence rates of up to 70%, requiring; repeated cystoscopies that are uncomfortable, costly, and require hospital visits. Alternative methods for; diagnostics and monitoring remain limited.; CELLECTED addresses this by developing a non-invasive method to detect cancer cells shed into urine,; although these are rare and difficult to identify.
Our approach uses sound waves to isolate and; concentrate these cells in a chemical-free manner, followed by advanced imaging and Artificial; Intelligence to identify patterns linked to cancer presence and progression. This method enables the; characterization of tumour features through exfoliated cells, reducing the need for invasive procedures; within the bladder. As a result, CELLECTED provides a more accessible and patient-friendly approach to; bladder cancer detection and monitoring.;
3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING:
extend the knowledge of the state of the art in machine learning/computer vision for bladder cancer imaging data;
identify and select the appropriate methods for the study in question;
develop the research capacity through the application of the selected methods;
exercise a critical spirit in the evaluation of the research process and the results obtained.
4. REQUIRED PROFILE: Admission requirements:
The awarding of the grant is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. The awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions.
Preference factors:
Experience in research projects, and writing of scientific papers.
Minimum requirements:
Experience in Computer Vision and machine learning.
5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and corresponding valuation: the first phase comprises the Academic Evaluation (AC), based on the criteria referred to in Article 12 of the Regulations for Grants of INESC TEC, while the second phase comprehends the Individual Interview (EI). All factors are evaluated on a scale of 0 to 100, taking into account the applicants' merit, suitability and conformity with the preference factors.
The weight of the AC factors are as follows: Academic Qualifications (FA, 30), Scientific Publications (PC, 20), Experience (EX, 20) and Motivation Letter (CM, 30). Candidates who score less than 50 points in the AC average will be considered excluded on absolute merit. The top five candidates approved on absolute merit will be qualified for the individual interview. The Final Grade (CF) is obtained by the weighted average of AC (80) and EI (20).
DISABILITY INCENTIVE Candidates who present a degree of disability equal to or greater than 90% will benefit from an incentive (20) in the score of the CV Assessment. Candidates who present a degree of disability equal to or greater than 60% and less than 90% will also benefit from an incentive (10) in the score of the CV Assessment. Said score may, in these cases, exceed 100 points. Candidates must demonstrate the degree of disability during the application, namely through the submission of the Multi-Purpose Medical Certificate of Disabili