About the position

A PhD fellowship Bayesian statistics is available at the Department of Mathematical Sciences at NTNU. The successful candidate will be offered a three-year position. The Department may offer a twelve-month extension as a teaching assistant.

The work place will be Trondheim.

Main duties and responsibilities

Bayesian methods provide a natural approach to formulate hierarchical and joint models. As such, these methods are particularly suitable to account for measurement error or misclassification error in variables of regression setups. Markov chain Monte Carlo (MCMC) sampling and integrated nested Laplace approximations (INLAs) have been successfully used in the past to formulate and fit such models. As an example, special models to account for measurement error in continuous covariates have been implemented in INLA. On the other hand, it is less clear how misclassification error, that is, error in binary or categorical covariates, can be tackled in INLA. However, misclassification error is ubiquitous, for example as a result of imperfect test performance in a medical context (limited sensitivity and specificity), or due to limited precision of GPS signals in animal telemetry studies, which leads to misclassified habitat types during the sampling process.

In this PhD project, the successful candidate will combine MCMC, INLA and importance sampling to efficiently fit models that account for misclassification error. Comparisons to pure MCMC sampling and non-Bayesian approaches will help putting the new developments into context.

In a second step, the PhD candidate will work on missing data problems, and take advantage of the fact that these can be interpreted as an extreme form of measurement or misclassification error. It will be shown how the Bayesian view helps unifying these two concepts, and that it is possible to tackle error and missing data problems in a single framework, which will not only lead to efficient algorithms to account for both simultaneously but will also enhance our understanding of missing data and measurement error.

Finally, the PhD student should make these methods accessible to a wide audience by implementing and disseminating them. This is an important step, because the use of methods to account for measurement error or missing data is often restricted by limitations of available software. The idea is to write an R-package and to apply the novel methods to real-world examples from our collaborators at the Centre for Biodiversity Dynamics (CBD) at NTNU, from the Department of Evolutionary Biology and Environmental Studies at the University of Zurich, Switzerland, and/or from other collaborators across Europe and the United States.

Qualification requirements

We are looking for a very highly motivated candidate with a sound background in statistics, with particular interest or experience in Bayesian methods. The PhD project is interdisciplinary, thus the candidate should be interested in developing and applying methods in collaboration with applied researchers from ecology, evolutionary biology or other fields outside mathematics.

Good communication skills and an independent, self-driven working style are essential. In addition, the project requires good programming skills, preferably in R. The candidate should be willing to learn how to develop and maintain R-packages.

The PhD-position's main objective is to qualify for work in research positions. The qualification requirement is completion of a master’s degree or second degree (equivalent to 120 credits) with a strong academic background in statistics or equivalent education with a grade of B or better in terms of NTNU’s grading scale. Applicants with no letter grades from previous studies must have an equally good academic foundation. Applicants who are unable to meet these criteria may be considered only if they can document that they are particularly suitable candidates for education leading to a PhD degree.

MSc students who expect to complete their master’s degree studies by summer 2020 are also encouraged to apply. Employment will then be postponed until the master’s degree is finished.

The applicants who do not master a Scandinavian language must document a thorough knowledge of English (equivalent to a TOEFL score of 600 or more).

The appointment is to be made in accordance with the regulations in force concerning State Employees and Civil Servants and national guidelines for appointment as PhD, post doctor and research assistant.

NTNU is committed to following evaluation criteria for research quality according to The San Francisco Declaration on Research Assessment - DORA.

Personal characteristics

  • High self-motivation
  • Intrinsic curiosity and open-minded attitude
  • Independent working style
  • Very good communication skills

In the evaluation of which candidate is best qualified, emphasis will be placed on education, experience and personal suitability, in terms of the qualification requirements specified in the advertisement.

We offer

Salary and conditions

PhD candidates are remunerated in code 1017 and are normally remunerated at gross from NOK 479 600 per annum before tax. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund.

The period of employment is 3 years with no teaching, but the Department may offer a 4th year with teaching and other duties for approximately 25% of the entire 4-year period.

Appointment to a PhD position requires admission to the PhD programme in mathematics; please see http://www.ntnu.edu/ie/research/phd for information about the PhD programme at NTNU.

As a PhD candidate, you undertake to participate in an organized PhD programme during the employment period. A condition of appointment is that you are in fact qualified for admission to the PhD programme within three months.

The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criterias in the latter law will be prohibited from recruitment to NTNU. After the appointment you must assume that there may be changes in the area of work.

General information

Working at NTNU

A good work environment is characterized by diversity. We encourage qualified candidates to apply, regardless of their gender, functional capacity or cultural background. Under the Freedom of Information Act (offentleglova), information about the applicant may be made public even if the applicant has requested not to have their name entered on the list of applicants.

The national labour force must reflect the composition of the population to the greatest possible extent, NTNU wants to increase the proportion of women in its scientific posts. Women are encouraged to apply. Furthermore, Trondheim offers great opportunities for education (including international schools) and possibilities to enjoy nature, culture and family life (http://trondheim.com/). Having a population of 200 000, Trondheim is a small city by international standards with low crime rates and little pollution. It also has easy access to a beautiful countryside with mountains and a dramatic coastline.

Video

Questions about the position can be directed to Associate Professor Stefanie Muff, e-mail: stefanie.muff@ntnu.no .

About the application:

The application must include the following:

  • Information about education background and work experience.
  • Any relevant publications. Joint work will only be considered provided that a short summary outlining the applicant's contributions is attached.
  • Certified copies of relevant transcripts and diplomas. Candidates from universities outside Norway are kindly requested to send a Diploma Supplement or similar documentation, which describes in detail the programme of study, the grading system, and the rights to further studies associated with the degree obtained.
  • Contact information for at least two references.
  • Documentation of fluency in the English language.

Please submit your application electronically via jobbnorge.no. Preferably, the attachments should be submitted as a single file.

Please refer to the application number 2019/35903 when applying.

Applicants who have been short-listed will be invited for interviews.

Application deadline: 01.12.2019.

NTNU - knowledge for a better world

The Norwegian University of Science and Technology (NTNU) creates knowledge for a better world and solutions that can change everyday life.

Department of Mathematical Sciences 

We are Norway’s largest university environment in mathematical sciences. The Department has a particular responsibility for all basis education in mathematical sciences for engineering and natural science students at NTNU. We focus on long-term basic research and applied research at a high international level. 
Our aim is to meet the society’s needs for mathematical and statistical expertise in business and public administration as well as in the research and education sector.  The Department of Mathematical Sciences is one of seven departments in the Faculty of Information Technology and Electrical Engineering .

Deadline 1st December 2019
Employer NTNU - Norwegian University of Science and Technology
Municipality Trondheim
Scope Fulltime
Duration Project
Place of service Trondheim

 

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Felix Emeka Anyiam

 

Research Officer & Data Analyst/Scientist

Centre for Health and Development

University of Port Harcourt (UNIPORT)

Top Floor, Medical Library Building

University of Port Harcourt Teaching Hospital (UPTH), Port Harcourt

River State, Nigeria.

 ORCID ID: http://orcid.org/0000-0003-2774-7406

 Skype ID: @felix.emeka.anyiam

tel: +234 (0) 806 499 5462                                                    

email: chd@uniport.edu.ng

http://www.uniport.edu.ng/centres/170-centre-for-health-and-development-chd.html

 I don't mind not knowing.  It doesn't scare me.  - Richard Feynman

To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of. -Ronald A. Fisher.

 

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