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

Organisation/Company INESC TEC Research Field Computer science » Modelling tools Mathematics » Applied mathematics Researcher Profile First Stage Researcher (R1) Application Deadline 11 Nov ***:59 (UTC) Country Portugal Type of Contract Temporary Job Status Full-time Hours Per Week 36 Offer Starting Date 9 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 within the scope of the project IGNIT with reference * (COMPETE-FEDER-**) co-funded by the ERDF
- European Regional Development Fund through Innovation and Digital Transition Program
- COMPETE *
* under the scope of Portugal *. GRANT DESCRIPTION
Type of grant: Research Grant (BI) General scientific area: COMPUTER SCIENCE,MATHEMATICS Scientific subarea: Applied mathematics,Modelling tools Grant duration: 6 months, starting on ** , with the possibility of being renewed until the end of the project. Workplace: INESC TEC, Porto, Portugal Maintenance stipend: * ou *, 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
As part of the IGNIT project, the research fellow will adapt metrics to characterize wildfire ignition causes, assess the quality of SGIF records, enrich these data with social, anthropological, economic and territorial information from other databases, and develop machine learning models to estimate the causes of ignitions that were not investigated or whose investigation was inconclusive, as well as to predict ignition types based on spatial patterns and socio-territorial factors. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING
Adapt standardized and quantitative metrics to quantify forest fire-ignition causes data records quality; Assess the quality of cause classification in fresh entries; Enhance the existing causes dataset in SGIF by integrating anthropological and social data derived from other sources (e.g. National Statistics Institute, PORDATA, fieldwork, and others sources); Use the existing causes dataset in SGIF to predict ignition causes investigated by traditional methods without success, by using machine learning methods; Use the existing causes dataset in SGIF to predict ignition causes not investigated, by using machine learning methods; Use analytics and machine learning methods to predict ignition types based on the results of a spatial analysis previously performed in a GIS environment. 4. REQUIRED PROFILE
Admission requirements Bachelor's and Master's degree in Industrial Engineering and Management or related field. The awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors Python programming; Experience and domain in the use of R in analyses involving GIS, Cartographic and Spatial Analysis Tools (QGIS, sf, Terra); Motivation for personal and professional development, in addition to the inclusion of the theme of this Grant in the objectives of the doctoral thesis.
Minimum requirements
PhD student (PRODEGI or PDEPP); Experience in the development of R scripts for data manipulation and preparation, and algorithms in applications within the scope of Integrated Rural Fire Management through R&D projects, company internship, or professional career; Experience with R/RStudio. 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, 50), Scientific Publications (PC, 10), Experience (EX, 30) and Motivation Letter (CM, 10). 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 (60) and EI (40). DISABILITY INCENTIVE
Candidates who present a degree of disability equal to or greater than 90% will ben