Kasetsart University

What you'll study

The PhD in Statistics combines advanced coursework, seminars and an original research dissertation. Students develop rigorous foundations in probability and inference, advanced modelling techniques and modern computational tools, with opportunities to apply methods in agriculture, environment, biology, economics and big-data contexts.

• Core topics: measure-theoretic probability, asymptotic statistical theory, advanced statistical inference and decision theory.
• Advanced methods: multivariate analysis, time series and forecasting, spatial and spatio-temporal statistics, survival analysis and longitudinal data methods.
• Computational and modern techniques: Bayesian statistics and MCMC, statistical machine learning, high-dimensional statistics, resampling and simulation methods, and statistical computing (R, Python and specialised libraries).
• Design and application: design of experiments, survey sampling, biostatistics and epidemiological methods, econometrics and applied regression modelling.
• Research components: advanced seminars, literature review, qualifying examinations, and a supervised original dissertation presenting novel methodological or applied contributions. Students are expected to present work at internal seminars and to pursue publication in peer-reviewed journals.

Entry requirements

Applicants are normally expected to hold a relevant master’s degree in statistics, mathematics, biostatistics, or a closely related quantitative discipline, with strong academic performance and demonstrated research potential. Candidates without a master’s but with an outstanding honours degree and significant research experience may be considered on a case-by-case basis.

• Academic transcripts from all prior tertiary study.
• A research statement or proposal outlining intended research topics and objectives.
• Curriculum vitae detailing academic background, research experience and relevant skills (programming, software, publications).
• Letters of recommendation (typically two or three) from academic or professional referees who can attest to research ability.
• Demonstrable competence in English; applicants may be asked to provide standardised test scores or university-validated evidence of proficiency where required.
• Some applicants will be invited to an interview or entrance assessment and should identify potential supervisors or research groups within the department.

Career prospects

Graduates of the PhD in Statistics move into a wide range of roles that demand high-level analytical and methodological expertise. Typical career paths include academic positions (lecturer, researcher), roles in public-sector research bodies and statistical agencies, and specialist positions in industry.

• Academia and research institutes: university teaching and research, postdoctoral fellowships, and leadership of research projects.
• Government and public policy: national statistical offices, ministries dealing with agriculture, health and the environment, and regulatory agencies requiring advanced data analysis.
• Industry: data science, quantitative analytics, risk and actuarial roles in finance and insurance, biostatistics in pharmaceutical and healthcare companies, and applied modelling in agriculture and environmental firms.
• Consulting and international organisations: consultancy firms, non-governmental organisations and international bodies that commission or conduct statistical research and program evaluation.

Why study at Kasetsart University

Kasetsart University offers a PhD environment with strong links to applied domains where statistics has immediate impact, notably agriculture, environmental science and biological research. The university combines established academic departments with interdisciplinary research centres and experimental facilities, facilitating collaborative projects and field-based data work.

• Research strengths and collaborations: opportunities to collaborate with agricultural research stations, environmental monitoring programmes and government agencies on applied statistical projects.
• Supervisory expertise: faculty members with expertise across theoretical statistics, Bayesian methods, spatial and environmental statistics, biostatistics and computational techniques.
• Training and resources: access to computational infrastructure, software support, specialised seminars and a research culture that encourages publication and conference participation.
• Location and networks: a large multi-campus university with active partnerships in industry and public research, offering broad exposure to real-world datasets and interdisciplinary teams.