数学博士研究奖学金

Scientific Advisor: Maria do Rosário De Oliveira Silva (ist12954) Co-advisor(s): Maria do Rosário De Oliveira Silva (ist12954), CEMAT e Departamento de Matemática, Instituto Superior Técnico, Universidade de Lisboa; Jorge Filipe Duarte Tiago (ist90590), CEMAT e Departamento de Matemática, Instituto Superior Técnico, Universidade de Lisboa; Maria da Conceição Esperança Amado (ist13493), CEMAT e Departamento de Matemática, Instituto Superior Técnico, Universidade de Lisboa. Organic Unit: Centre for Computational and Stochastic Mathematics Scholarship Theme: Pandemic Intelligence: Modelling and Monitoring COVID-19 to Support Public Health Decisions Duration: 6 months Maximum Duration Including Renewals: 6 months Objectives To develop and validate a time-dependent modelling and forecasting framework that captures the evolving dynamics of the COVID-19 pandemic and predicts the impact of public health interventions, including vaccination strategies, nonpharmaceutical measures, and the emergence of new variants. The framework is intended to support adaptive resource planning (e.g., hospital and ICU bed allocation) and to monitor the effectiveness of interventions over time. The work is organized around three specific objectives: (i) Data preparation: Integrate, harmonize, and clean historical and current COVID-19 epidemiological and vaccination data, ensuring suitable temporal structure for time-dependent modelling. (ii) Modelling: Design and calibrate epidemiological models with time-varying parameters to represent transmission dynamics, vaccination effects, and intervention scenarios. (iii) Validation: Benchmark model outputs against historical and current data using time-aware validation protocols and appropriate error metrics to assess predictive accuracy, robustness, sustainability, and generalizability of the fitted models. Work Plan The work is organized into four main tasks, aligned with the project's specific objectives: Task 1: Literature review and data collection. Review the state of the art on time-dependent COVID-19 modelling and forecasting, and identify and collect relevant epidemiological, vaccination, and intervention data, and parametric choices from national and international cases. Task 2: Data preparation. Integrate, harmonize, and clean the available data, ensuring a temporal structure suitable for time-dependent modelling. Task 3: Model development and fitting. Design, implement, and fit epidemiological models with time-varyingparameters to the data, combining mechanistic and data-driven approaches as appropriate, and apply them to relevantintervention scenarios.Task 4: Validation and dissemination. Evaluate model performance using time-aware validation protocols andappropriate error metrics and prepare the resulting outputs for scientific dissemination and scientific paper writing. Contest Procedure Applications must be exclusively submitted on the admissions platform of the Instituto Superior Técnico at https://fenix.tecnico.ulisboa.pt/fenixedu-admissions and requires registration and validation of the candidate's identity. Applications are only accepted when the form available in the platform is correctly filled, submitted and locked withoutany validation errors. The mandatory documentation to submit in the scholarship aplication includes: Curriculum Vitae Proof of Qualifications (or declaration of honor in case you do not yet have the certificate) Proof of Registration/Enrolment The application submission deadlines can be viewed in the admissions platform. The results of the contest will be made available in the same admissions platform.

Requirements

Research Field Mathematics » Computational mathematics Education Level Master Degree or equivalent

Skills/Qualifications Admission Requirements Applicants must hold a master’s degree in Applied Mathematics, Statistics, Data Science, Artificial Intelligence or arelated field and be enrolled in a Doctoral Programme in Statistics and Data Mathematics, Doctoral Programme inMathematics or a related field.Basic knowledge of biostatistics, statistical learning, statistical methods in data mining, multivariate analysis, timeseries, machine learning, analysis of linear models, computational statistics, optimisation, Bayesian statistics, andsymbolic data analysis.Specific requirements: Proficiency in Python and R.

Additional Information

Benefits Monthly Maintenance Allowance: €1,359.64 Funding Entity: European Union (EU)

Eligibility criteria Admission Requirements Applicants must hold a master’s degree in Applied Mathematics, Statistics, Data Science, Artificial Intelligence or arelated field and be enrolled in a Doctoral Programme in Statistics and Data Mathematics, Doctoral Programme inMathematics or a related field.Basic knowledge of biostatistics, statistical learning, statistical methods in data mining, multivariate analysis, timeseries, machine learning, analysis of linear models, computational statistics, optimisation, Bayesian statistics, andsymbolic data analysis.Specific requirements: Proficiency in Python and R.

Selection process Contest Evaluation Method(s) Curricular evaluation weighted to 100% on a scale of 20 points with a minimum of 15 points needed for admission. The minimum final grade needed for admission is 15 points. Conditions for the Contest Evaluation 35% CV and 65% professional experience Composition of the Selection Jury Jury President: Maria do Rosário De Oliveira Silva (ist12954) Jury Members: Jorge Filipe Duarte Tiago (ist90590), CEMAT e Departamento de Matemática, Instituto SuperiorTécnico, Universidade de Lisboa; Maria da Conceição Esperança Amado (ist13493), CEMAT e Departamento deMatemática, Instituto Superior Técnico, Universidade de Lisboa. In case the president of the jury is unable to preside, they will be replaced by one of the jury members.

Additional comments Applicable Laws and Regulations Law No. 40/2004, of 18 August (Statute of Scientific Research Fellow), in its current wording; IST Regulation forResearch Scholarships, available at https://drh.tecnico.ulisboa.pt/files/sites/45/despacho_8532_regulamento_bolsas.pdf

Workplace: The work will be carried out at the Department of Mathematics of the Instituto Superior Técnico – AlamedaCampus, University of Lisbon.

Website for additional job details https://drh.tecnico.ulisboa.pt/bolseiros/recrutamento/

Organisation/Company: Instituto Superior Técnico Department: Direção de Recursos Humanos Research Field: Mathematics » Computational mathematics Researcher Profile: First Stage Researcher (R1) Positions: Master Positions Type of Contract: Not Applicable Job Status: Not Applicable Country: PT