Lund University, Faculty of Science, Centre for Mathematical Sciences

Lund University was founded in 1666 and is repeatedly ranked among the world’s top 100 universities. The University has 42 000 students and 7 400 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition.

The Faculty of Science conducts research and education within Biology, Astronomy, Physics, Geosciences, Chemistry, Mathematics and Environmental Sciences. The Faculty is organized into ten departments, gathered in the northern campus area. The Faculty has approximately 1900 students, 330 PhD students and 700 employees.

"Statistical methods for intractable likelihoods with applications to stochastic differential equation models for protein folding"

Job Assignments
In this fully funded 4-5 years PhD studentship you will conduct research on Monte Carlo methods to enable statistical inference for models having an intractable likelihood function. The project is motivated by and aims at applications to high dimensional protein folding data. 

In many classical statistical applications, likelihood inference is often the preferred method for statistical analysis for many and well known reasons. However model complexity, such as nonlinear dynamics and/or latent states, can make it difficult (if not outright impossible) to pursue the likelihood in explicit mathematical form. Recent years have provided breakthroughs in computational statistics for dealing with models having intractable likelihoods (examples are approximate Bayesian computation (ABC) and sequential Monte Carlo methods).

Aim of this research project is to further develop these methods and use them in applications to e.g. biomedical research. In fact the proposed project has some degree of interdisciplinarity and is also concerned with stochastic dynamic models for protein folding described via stochastic differential equations. Protein folding is an important biological process which is also associated with a wide range of human diseases.    

You will be based at the Centre for Mathematical Sciences at Lund University (Sweden). In addition to develop your research in Monte Carlo statistical methods you will collaborate with experts who have domain-specific expertise in protein dynamics and biostatistical modelling at Copenhagen University.

The project is supervised by Dr. Umberto Picchini and offers the scope for personal development to students who are interested in computational and applied statistics. Further details may be obtained from Umberto Picchini (umberto@maths.lth.se).

The position covers four years of postgraduate studies (roughly two years of course work and two years of research) and one year of departmental duties, mainly teaching in mathematical statistics.

Eligibility
Students with basic eligibility for third-cycle studies are those who have completed a second-cycle degree, have completed courses of at least 240 credits, of which at least 60 credits are from second-cycle courses, or have acquired largely equivalent knowledge in some other way, in Sweden or abroad.

Specific admission requirements To be admitted to the third-cycle programme in Mathematical Statistics the student must have passed independent study courses in mathematics comprising at least 60 credits including at least 30 credits in mathematical statistics. A degree project of at least 30 credits is also required. Equivalent knowledge acquired through corresponding programmes will be assessed individually.

Successful applicants should, in addition to the basic qualifications, have at least 3 courses in (mathematical) statistics, including one course in stochastic processes; the applicant should also have at least one course in programming or numerical analysis. Further programming experience, preferably in languages such as Matlab or R, as well as proficiency in English is highly meritorious. Applicants with an interest in Bayesian statistics, Monte Carlo methods, stochastic differential equations, or in the analysis of protein data are particularly encouraged to apply.

Application
When applying you should upload a cover letter, a current CV and a scanned copy of your academic transcript. Additional supporting documents such as letters of recommendation could also be submitted, but are not required.

Basis of Assessment
The employment of doctoral students is regulated in the Swedish Code of Statues 1998: 80. Only those who are or have been admitted to PhD-studies may be appointed to doctoral studentships. When an appointment to a doctoral studentship is made, the ability of the student to benefit from PhD-studies shall primarily be taken into account. In addition to devoting themselves to their studies, those appointed to doctoral studentships may be required to work with educational tasks, research and administration, in accordance with specific regulations in the ordinance.

In selecting between applicants who meet the requirements, their ability to benefit from the course or study programme shall be taken into account. However, the fact that an applicant is considered able to transfer credits from prior courses and study programmes or for professional or vocational experience may not alone give the applicant priority over other applicants.

The following selection criteria will be applied: Study record from undergraduate and Master’s courses or the equivalent. The breadth, depth and relevance of undergraduate and Master’s courses or the equivalent. The quality of the degree project and other independent work. Other knowledge and skills of relevance to the research specialization.

Type of Employment
Limit of tenure, four years according to HF 5 kap 7§

Enquiries
For informal enquiries contact Dr. Umberto Picchini (umberto@maths.lth.se).

Type of employment Temporary position longer than 6 months
First day of employment 2016-10-01
Salary Monthly salary
Number of positions 1
Working hours 100 %
City Lund
County Skåne län
Country Sweden
Reference number PA2016/1537
Contact
  • Umberto Picchini, +46462229270
Published 11.May.2016
Last application date 09.Jun.2016 11:59 PM CET

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