Public Health Sciences
Doctoral programme (PhD)
A.Y. 2026/2027
Study area
Medicine and Healthcare
PhD Coordinator
The doctoral programme in Public Health Sciences aims to form researchers who are able to design, undertake and critically interpret research and advanced training projects and to assess health status and prevent the spread of transmittable and chronic-degenerative diseases within the population; promote health in individuals and populations; disseminate scientific culture and applicable research methodologies.
The internationalisation process foresees the exchange of students and professors with doctoral students, research centres and foreign institutions and the organisation of courses held by foreign experts.
The specific objectives of the doctoral programme include the development of research methodologies in the area of health, both laboratory and clinical; in the area of prevention policies focusing on the public and on particular groups at risk; in the context of services to the population for protecting and promoting individual health; in a transversal and transdisciplinary context that recognizes that the health of a populations is closely linked to that of animals and the environment ('one health' approach) and acquisition of a solid methodological grounding in the quantitative disciplines required to correctly apply and develop methods for the critical interpretation of basic, clinical, medical and epidemiological research findings.
The internationalisation process foresees the exchange of students and professors with doctoral students, research centres and foreign institutions and the organisation of courses held by foreign experts.
The specific objectives of the doctoral programme include the development of research methodologies in the area of health, both laboratory and clinical; in the area of prevention policies focusing on the public and on particular groups at risk; in the context of services to the population for protecting and promoting individual health; in a transversal and transdisciplinary context that recognizes that the health of a populations is closely linked to that of animals and the environment ('one health' approach) and acquisition of a solid methodological grounding in the quantitative disciplines required to correctly apply and develop methods for the critical interpretation of basic, clinical, medical and epidemiological research findings.
Tutte le classi di laurea magistrale - All classes of master's degree
Department of Clinical Sciences and Community Health - Via della Commenda 19 - 20122 Milano
- Doctoral programme coordinator: Parazzini Fabio
[email protected] - Website
https://discco.unimi.it/it/node/16663/ - Main offices
Dipartimento di Scienze cliniche e di comunità - Via Commenda, 19 - Milano
| Title | Professor(s) |
|---|---|
| Towards a more rational use of infertility treatments: studies on models validation, cost-effectiveness and cost-benefits
Requirements: Awareness on infertility clinical issues and good knowledge of statistics |
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| Informed consent for clinical and research activities. Evaluations for quality and risk assessmet system | |
| Phylogenesis and phylodynamics of the emerging infectious pathogens
Requirements: Expertise in the molecular biology techniques with special refer to the next generation sequencing and basic knowledge in bioinformatics and phylogenetic analysis |
|
| Early biomarkers of unilateral ureteropelvic junction obstruction in children
Requirements: Laboratory experience in cellular and molecular biology. Good knowledge of statistics |
|
| Generation of kidney organoids to study the effects of the exposome on the development of chronic kidney disease
Requirements: Laboratory experience in molecular biology and -omics. Good knowledge of statistics |
|
| Telemedicine: approaches and tools towards a value-based healthcare | |
| Operation management and service management in healthcare sector (patient flow logistics, patient experice) | |
| Co-production of health service | |
| Value based healthcare: approaches and tools | |
| Functional ageing and inequality: Develop a function-based ageing index and quantify how socio-economic and migration-related inequalities shape ageing trajectories and labour/social participation
Requirements: Quantitative research methods, survey data analysis, strong statistical knowledge, and programming skills in R or Python |
|
| Evaluation of One Health strategies: Quantify the implementation and impact of integrated human–animal–environment interventions on health outcomes.
Requirements: Quantitative and qualitative research methods, strong statistical knowledge, and programming skills in R or Python. |
|
| Health system resilience and pandemic preparedness in Europe: Develop quantitative resilience and preparedness indices and evaluate their association with pandemic outcomes
Requirements: Quantitative and qualitative research methods, strong statistical knowledge, and programming skills in R or Python |
|
| Multisectoral interventions to reduce the burden of NCDs: Evaluate the effectiveness and cost-effectiveness of multisectoral policies in reducing NCD burden and healthcare costs
Requirements: Quantitative and qualitative research methods, strong statistical knowledge, and programming skills in R or Python |
|
| Measuring and reducing healthcare’s climate footprint: Quantify the carbon footprint of health systems and evaluate the cost-effectiveness of decarbonization strategieswithout compromising quality of care
Requirements: Quantitative and qualitative research methods, strong statistical knowledge, and programming skills in R or Python |
|
| Design, development and evaluation of predictive regression models with high-dimensional data. Model selection
Requirements: Good knowledge of statistical methods and survival analysis |
|
| Design and development of predictive models for clinical applications using Artificial Intelligence methods
Requirements: Good knowledge of statistical methods and survival analysis |
|
| Target trial emulation: the use of causal inference techniques to evaluate the effect of a treatment using real‑world data
Requirements: Basic knowledge of survival analysis |
|
| Use of generalized linear (and non-linear) models for treatment evaluation using clinically useful measures and time-to-event outcomes
Requirements: Good knowledge of statistical methods and statistical software |
|
| Evaluation of the nutritional quality and microbiological and toxicological safety of foods, with focus on valorization of by-products to enhance the sustainability of production chains.
Requirements: Knowledge in nutrition, microbiology, molecular biology, chemistry and food technology |
|
| Wastewater-based epidemiology: metagenomic approaches for monitoring antibiotic resistance in wastewater and surface waters
Requirements: Knowledge in microbiology, molecular biology and epidemiology |
|
| Molecular epidemiology of antimicrobial resistance in a One-Health perspective. Sampling, sequencing and comparison of resistance genes from human and animal pathogenic bacteria in Lombardy
Requirements: Knowledge in microbiology, molecular biology and epidemiology |
|
| A One Health approach to the epidemiology of zoonotic viral hepatitis caused by Hepevirus in Italy: the role of suids, rodents, wastewater, food matrices, and viral typing using third-generation sequencing and bioinformatic analysis
Requirements: Competences in microbiology and molecular omics techniques |
|
| Use of routine data base to monitor the quality of the obstetric care and the analysis of the geographic and temporal trends of obstetric diseases
Requirements: Exerience in clinical research, public health and medicaal statistics |
|
| Usage of big databases (administrative databases o more clinical databases togheter) to evaluate the ability (in terms of feasibility and accuracy) of the attribute matching methodology to predict the events risk in different clinical contensts, comparing prognostic scores already pubblished in literature
Requirements: Knowledge of medical statistic (base level) and good skills on using softwares for the statistical analysis and management of big databases (SAS, MS Access) |
|
| Statistical methods for primary studies and metanalysis on diagnostic accuracy
Requirements: Methods for contingency tables analysis and knowledge on linear model |
|
| Projecting, implementing and assessing the effectiveness of nursing interventions in different clinical situations, in hospital and outpatient clinics. Projecting and implementing interventions for reducing nursing-related clinical risk
Requirements: Nursing competence (clinical, educational, organizational or researh-oriented) |
|
| Psychometric methodology in exposed to occupational stress
Requirements: Expertise in epidemiological studies with evaluation of psychological wellbeing in workers (health care or other sectors) |
|
| Infections of public health impact: epidemiology and prevention
Requirements: Basic knowledge of techniques used in virology Curriculum: Infezioni di rilevante impatto per la Sanità Pubblica: epidemiologia e prevenzione |
|
| Occupational and organizational risk factors and psychological wellbeing | |
| Methods for assessing the efficacy of workers' health prevention and promotion in the workplace |
M. Bonzini
|
| Physiopathogenetic machanisms of occupatonal diseases | |
| Air pollution exposure and molecular markers
Requirements: Expertise in epigenetics, microbioma, or molecular biology |
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| Environmental Exposure and salute mentale
Requirements: Knowledge of main environmental exposures and health outcomes |
|
| Within the EPIGENESIS project, the effects of the exposome on health will be studied both at the individual and population level through the use of advanced "omics" technologies, such as epigenetics, metabolomics and single-cell RNAseq. The search for epigenetic biomarkers able to synthesize the complex information derived from the exposome will assume particular relevance
Requirements: Expertise in epigenetics, microbioma, or molecular biology |
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| Ageing and work: effects of workers' ageing and strenous working condition on biological age and work ability
Requirements: Skills in the field of the evaluation of health effect associated to occupational exposures |
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| Study of the role of diet in the development of various types of cancer, through statistical models capable of evaluating the simultaneous effect of a wide range of macro- and micronutrients.
Requirements: Programming skills in SAS or R languages |
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| Analysis, interpretation and prediction of mortality and incidence data
Requirements: Knowledge of statistical programming languages SAS or R, and of the main analysis techniques used in this field (e.g., linear models and joinpoint) |
|
| Statistical models for the analysis of longitudinal data in clinical epidemiology: applications in cystic fibrosis and respiratory diseases
Requirements: Basic knowledge of generalized linear models. |
|
| Collaborative reanalysis of epidemiological data, including epidemiological inference
Requirements: Methods for meta- and pooled analyses, including mixed-effects models. Knowledge of SAS and R |
|
| Effects of a diet adhering to the Mediterranean diet on the composition of breast milk
Requirements: Good programming skills in SAS or R. Good knowledge of basic statistics |
|
| A posteriori dietary pattern in nutritional epidemiology: problems of statistical analysis and epidemiological interpretation
Requirements: Good programming skills in SAS or R. Multivariate statistical knowledge |
|
| Issues of epidemiological models, with particular reference to large food or nutrient databases
Requirements: Good programming skills in SAS or R. Good knowledge of basic statistics |
|
| Shared and covariate-specific a posteriori dietary patterns for the assessemnt of reproducibility of dietary patterns across studies, countries, time, or major covariates
Requirements: Good programming skills in SAS or R. Multivariate statistical knowledge |
|
| Prevention strategy in global health: mass vaccination, epidemiological surveillance and rapid response, monitoring of vaccine-preventable diseases, innovative screening. strategies
Requirements: Basic knowledge of epidemiology and prevention, experience in the molecular biology laboratory |
|
| Molecular epidemiology of viral infections of major public health impact
Requirements: Basic experiences in the molecular biology laboratory |
|
| Advanced statistical models for heterogeneous data in clinical and psychological (mental health) settings. Integration of clinical, behavioral and longitudinal data for outcome analysis and prediction.
Requirements: Knowledge of main supervised and unsupervised statistical learning techniques, of mixed-type data methods, distance-based techniques and robust statistics and R |
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| Distance-based methods, clustering for mixed-type data and robust statistical approaches for the analysis of complex clinical and behavioral data.
Requirements: Knowledge of main supervised and unsupervised statistical learning techniques, of mixed-type data methods, distance-based techniques and robust statistics and R |
|
| Effects of environmental biodiversity on the spread of vector-borne infections
Requirements: Knowledge of mathematical or statistical modelling, and basic GIS analyses, and programming in R |
|
| Clinical competence assessment frameworks in Health Care Organization and impact analysis | |
| Topics and methods for clinicians engagement in Health Care Organizations | |
| Organizational development and design in healthcare organizations: methods and tools for organizational check up and needs analysis, organizational models and structures design. | |
| Digital transformation in managing healthcare organizations: determinants and impacts | |
| Analysis of molecular data from Third Generation Sequencing of DNA and RNA for the evalutaion of bioprofiles in epigenetics
Requirements: Good experience in R programming. Good statistical knowledge |
|
| Multivariate analysis of envirnomental exposomic data
Requirements: Knowledge of the regression analysis, of the structural model analysis and of the multivariate statistical methods at an advanced level |
|
| Development of net and relative survival models for evaluating of incidence and mortality data from observational studies in clinical and environmental epidemiology.
Requirements: Good statistical knowledge |
|
| Assessment of the benefits and harms of health interventions by conducting systematic reviews in different clinical areas (internal medicine, cardiology, oncology, neurology, orthopaedics and rehabilitation, public health).
Requirements: Background in biostatistics and public health. |
|
| Application of the principles, concepts and methods of evidence-based medicine to the world of medical information.
Requirements: Background in biostatistics and public health, and editing. |
Enrolment
Places available: 5
Call for applications
Please refer to the call for admission test dates and contents, and how to register.
Application for admission: from 11/05/2026 to 10/06/2026
Application for enrolment: from 06/07/2026 to 10/07/2026
Attachments and documents
Following the programme of study
Contacts
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