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The following positions are currently open for applications. Please email TB MAC with details to post a TB Modelling job here


McGill University & McGill University Health Centre Postdoctoral Fellowship in Tuberculosis Diagnostics Research, Montreal, Canada

Applications are invited from recent PhD or MD graduates for a one year, full-time fellowship, starting October 2015.

Role

The postdoctoral fellow will function as a junior investigator, participating fully in teaching, research and related scholarly activities and would be encouraged to apply for independent salary and research funding. Specifically, the fellow would act as a junior investigator on a variety of projects relating to TB diagnostics, including meta-analyses, modeling, and in-country field research. The fellow will provide oversight at the collaborating international sites, conduct data management and analysis, prepare manuscripts, assist in writing grant proposals, and conduct methodological/statistical projects on diagnostic research. Travel abroad will be required.

Important note: This is NOT a laboratory-based (bench science) fellowship. The focus is on applied implementation research in clinical and field settings, use of novel methodological and statistical approaches, and evidence-based TB policies and practices. Applicants interested in basic science research on diagnostics will not be eligible for this fellowship.

Fellowship

The salary range is $45,000 - $55,000 per annum, depending on qualifications and research experience. Extended Health and Dental Insurance will be made available through the Post-Graduate Student’s Society. Depending on funding availability, the fellowship may be extended by another year.

Applications

Interested applicants should submit an application via email, consisting of a cover letter (including a statement of research interests and prior experience in TB diagnostic research), Curriculum Vitae, contact information of 2 relevant referees, along with 3 sample TB publications to:


Madhukar Pai, MD, PhD
Canada Research Chair in Translational Epidemiology & Global Health
Professor of Epidemiology, McGill University
Director, McGill Global Health Programs
Associate Director, McGill International TB Centre
McGill University
1020 Pine Avenue West, Montreal, QC, Canada, H3A 1A2

Email: madhukar.pai@mcgill.ca

This position will be advertised until a suitable candidate is identified. Information on postdoctoral fellowship regulations, including information on healthcare and benefits, at McGill University is available at: http://www.mcgill.ca/gps/postdocs/

More information about the Fellowship can be found here:

http://www.medicine.mcgill.ca/epidemiology/pai/documents/Postdoc_Ad_TB_Dx_Research_2015.pdf

 

Expired

The following positions are now closed.


EPSRC funded PhD in Mathematics: Statistical Epidemiology - Calibration of complex mathematical models for bovine tuberculosis transmission in Great Britain *

Closing date: 13th February 2015

Ref: *1723*

Bovine tuberculosis (bTB) is an important socio-economic disease in Great Britain, currently costing the government over £100 million per year in testing and compensation alone. Disentangling the key components of the transmission process is hard, due to confounding factors such as imperfect testing, large-scale cattle movements and environmental reservoirs of infection. Mathematical models can help us to explore these processes, but fitting these models to the available data is challenging, due to the size and complexity of both the models and data sets.

State-of-the-art statistical techniques, such as reversible-jump Markov chain Monte Carlo (RJ-MCMC) have been employed for various infectious disease models to enable the hidden processes to be inferred and mapped to the observed data. However, these approaches are hard to implement and optimise, are extremely computationally intensive, and do not always explore the parameter space efficiently (particularly for multimodal or heavy-tailed distributions).

In this project you will explore recent developments in history matching and stochastic emulation as a means of calibrating complex models to large-scale data sets. This overcomes some of the complexities of RJ-MCMC approaches by using simulation models to exclude parts of the parameter space where the model is unable to fit the observed data. The use of emulators (as a proxy for the complex simulation model) allows for efficient exploration of the parameter space. These techniques will be used explore the behaviours of competing models, and will provide important insights into the hidden processes that underpin bTB transmission and hamper control.

The deadline for applications is 13th February 2015.

For full details please see the website:

http://www.exeter.ac.uk/studying/funding/award/?id=1723


WHO consultancy: predictive statistical modelling to inform TB incidence, prevalence and mortality disease burden estimate

Closing date: Monday 22nd December 2014

Background

In June 2006, the Global TB Programme (GTB) in the World Health Organization (WHO) established a Global Task Force on TB Impact Measurement, with the TB monitoring and evaluation (TME) team in GTB as the secretariat. The mandate of the Task Force is to produce a robust, rigorous and widely-endorsed assessment of whether the 2015 targets set for TB control are achieved at global level and for each WHO region; to regularly report on progress towards these targets in the years leading up to 2015; and to help build national capacity in monitoring and evaluation. A wide range of technical, financial and development agencies, countries and individual experts are engaged in the work of the Task Force. Full details can be found on the Task Force website.

Three major strategic areas of work have been defined by the Task Force. These are:

1. Strengthening routine surveillance of TB cases and deaths towards the goal of directly measuring the burden of TB (cases and deaths) from notification and vital registration data;

2. National population-based surveys of the prevalence of TB disease in 22 global focus countries (13 in Africa, 9 in Asia);

3. Periodic review and updating of methods used to translate surveillance and survey data into estimates of TB incidence, prevalence and mortality.

In 2015 (likely March/April), GTB/TME will organize a global consultation on methods used to estimate TB incidence, prevalence and mortality i.e. a consultation related to the Task Force’s third major strategic area of work. This is essential to review and update where appropriate the methods used to produce TB burden estimates, in advance of the final assessment of whether 2015 targets were met (this will be undertaken in late 2015/early 2016). In advance of this consultation, various preparatory work is required. This is summarized below, for TB incidence, prevalence and mortality separately.

A. TB incidence

Three alternative options (compared with current methods used by WHO) need to be investigated:

1) Deterministic modelling of incidence, with data from nationwide population-based surveys of TB prevalence used as the key input – work already commissioned via an APW.

2) Predictive ecological modelling of incidence estimated from TB case notification and vital registration data (from middle and high income countries), as well as data on other established risk factors – work to be commissioned as part of this APW.

3) Bayesian framework using available input data (prevalence surveys and vital registration) and defining prior distributions for incidence based on elicited expert opinion – work to be commissioned in a separate APW.

B. TB prevalence

Two alternative options (compared with current methods used by WHO) need to be investigated:

1) Predictive ecological modelling of prevalence in low and middle-income countries with a predicted prevalence of over 0.1% but without data from a national TB prevalence survey, using national TB prevalence survey data from countries in which a survey has been undertake recently – work to be commissioned as part of this APW.

2) Bayesian framework using available input data (prevalence surveys and vital registration) and defining prior distributions for incidence based on elicited expert opinion – work to be commissioned in a separate APW.

C. TB mortality

Two alternative options (compared with current methods used by WHO) need to be investigated:

1) Predictive ecological modelling of mortality estimated from vital registration data (from middle and high income countries) and established risk factors, and applied to low- income countries – work to be commissioned as part of this APW.

2) Bayesian framework using available input data (prevalence surveys and vital registration) and defining prior distributions for incidence based on elicited expert opinion – work to be commissioned in a separate APW.

This APW covers the work required for A2, B1 and C1.

Tasks

In consultation with the senior epidemiologist and statistician in TME:

1. Develop a predictive ecological model for TB incidence that uses available data from TB case notification and vital registration data (from middle and high income countries) to predict TB incidence. The model should include documentation of uncertainty, and allow for production of estimates disaggregated by age, sex and HIV status where possible. Subtasks include:

a. Collation of relevant TB case notification and vital registration data and relevant risk factor data with support from GTB/TME;

b. Development of first draft of model;

c. Propagate uncertainty to estimates of incidence produced by the model;

d. Document the model code;

e. Investigation of how to add the time dimension (time series) to the model;

f. Investigation of how to disaggregate prevalence by age, sex and HIV status.

g. Refinement of model based on feedback provided at global consultation.

2. Develop a predictive ecological model for TB prevalence that uses available data from national TB prevalence surveys to predict TB prevalence in low and middle- income countries with predicted prevalence of over 0.1% where national surveys have not been implemented. Subtasks include:

a. Compilation of relevant data from TB prevalence surveys and relevant risk factor data with support from GTB/TME;

b. Development of first draft of model;

c. Propagate uncertainty to estimates of prevalence produced by the model;

d. Document the model code;

e. Investigation of how to add the time dimension (time series) to the model;

f. Investigation of how to disaggregate prevalence by age, sex and HIV status.

g. Refinement of model based on feedback provided at global consultation.

3. Develop a predictive ecological model for TB mortality (among HIV-negative individuals) that uses vital registration data from middle and high-income countries and established risk factors for TB mortality to estimate TB mortality in countries without vital registration data (mostly low-income countries). The model should include documentation of uncertainty, and allow for production of estimates disaggregated by age and sex where possible. Sub-tasks include:

a. Collation of all relevant vital registration and relevant risk factor data with support from GTB/TME;

b. Development of first draft of model;

c. Propagate uncertainty to estimates of mortality produced by the model;

d. Document the model code;

e. Investigation of how to add the time dimension (time series) to the model;

f. Investigation of how to disaggregate mortality by age and sex.

g. Refinement of model based on feedback provided at global consultation.

Expected Deliverables (with timelines)

1. Task 1

2. Task 2

a. Develop predictive ecological model of TB incidence (end March, so that it can be presented at the global consultation);

b. Presentation and short background paper detailing methods including computer code, goodness of fit, a discussion of model adequacy and recommendations on the use of the model in relation to existing methods used by GTB/TME (end March 2015, so that the background paper can be distributed to those participating in the global consultation in advance);

c. Final version of ecological model that addresses feedback provided in global consultation (June 2015).

a. Develop predictive ecological model of TB prevalence (end March, so that it can be presented at the global consultation);

b. Presentation and short background paper detailing methods including computer code, goodness of fit, a discussion of model adequacy and recommendations on the use of the model in relation to existing methods used by GTB/TME (end March 2015, so that the background paper can be distributed to those participating in the global consultation in advance);

c. Final version of ecological model that addresses feedback provided in global consultation (June 2015).

3. Task 3

a. First draft of predictive ecological predictive model of HIV-negative mortality (end March 2015, so that it can be presented at the global consultation);

b. Presentation and short background paper detailing methods including computer code, goodness of fit, a discussion of model adequacy and recommendations on the use of the model in relation to existing methods used by GTB/TME (end March 2015, so that the background paper can be distributed to those participating in the global consultation in advance);

c. Final version of ecological model that addresses feedback provided in global consultation (June 2015).

Profile of individual/team sought

Essential qualifications, knowledge, skills and experience

  • An advanced degree (preferably PhD level) in statistics;
  • Considerable experience and expertise in the development of predictive statistical models, including the documentation of uncertainty;
  • Excellent knowledge and understanding of TB epidemiology;
  • Expertise in data analysis, including in the context of TB data;
  • Strong analytical and communication (oral and written) skills;
  • Expert knowledge of STATA or R;
  • Excellent English.

Desirable experience

  • Knowledge of the work of the WHO Global Task Force on TB Impact Measurement;
  • Experience in the development of time series models.

Other essential requirements

  • Available to produce the first draft of the work by end March 2015 (a later date is not possible) and complete the work by end June 2015.

Budget

Interested applicants should submit their required budget as part of their proposal (see next section, “How to apply”).

How to apply

Interested applicant(s) should submit their proposal to undertake the work, including an explanation of why they are interested in the work, a summary of how they would approach the work, and how their profile relates to the essential and desirable requirements, to Charalambos Sismanidis (sismanidisc@who.int) and Pam Baillie (bailliep@who.int) at the latest by Monday 22 December (and preferably earlier).


Postdoctoral Opportunity for a Computational Scientist/Simulation Modeler

Closing date: Friday 2nd January 2015

Project Description

Applications from PhD or other doctoral-level degree holders are welcomed for a full-time, NIH-funded project focused on developing new simulation models to study the impact and cost –effectiveness of novel strategies for controlling tuberculosis epidemics in high HIV-burden settings. This project is an integral element of a joint research program of investigators at Yale School of Public Health and Harvard School of Public Health. Our team of investigators includes infectious disease epidemiologists, health economists, decision analysts and operations researchers.

We are currently seeking a postdoctoral associate who is eager to work with us to develop agent-based simulation models that leverage recent advancements in High Performance Computing technologies. While not essential, previous experience with transmission dynamic models of infectious diseases of humans or animals, is ideal.

The successful applicant will be expected to participate as lead author in publications arising from this work and to communicate findings in oral presentations at national and international conferences. We are seeking candidates willing to commit to at least 2 years of work on this project. We are eager to speak with candidates that are passionate about applying their existing scientific computing and programming skills to help address an urgent public health problem.

Qualifications and Skills

  • Doctoral degree in Computer Science, Computational Biology, Statistics, Biostatistics, Applied Mathematics, Operations Research, or related disciplines.
  • Experience with parallel and distributed computing, and using High Performance Computing clusters (Windows and Linux).
  • Strong programming skills in C++ and familiarity with .Net Framework, Visual C#, and Visual C++.
  • Working knowledge of numerical analysis, statistical and simulation methods.
  • Experience with object-oriented, discrete-event, and agent-based simulation modeling is highly desirable.
  • Experience with designing, implementing, and analyzing large-scale simulation models is highly desirable.
  • Experience with Matlab and R is desirable.
  • Candidates would be asked to provide evidence of an already functional application they have designed and implemented.

Benefit and Compensation Information

We offer a competitive salary and benefits package. Compensation will be commensurate with experience.

To apply:

Please submit a cover letter along with an updated resume/CV with contact information for 3 professional references and apply online at:

https://academicjobsonline.org/ajo/jobs/4795

For any additional information, please visit www.yale.edu/ysph and contact either:

Associate Professor Ted Cohen, Department of Epidemiology of Microbial Diseases, Yale School of Public Health (theodore.cohen@yale.edu)

Or

Professor Josh Salomon, Department of Global Health and Population, Harvard School of Public Health (jsalomon@hsph.harvard.edu)

Yale values diversity in its faculty, students, and staff and especially encourages applications from women, persons with disabilities, protected veterans, and underrepresented minority scholars


Opportunity: Postdoctoral Fellow in Tuberculosis Epidemiology

Description

Applications are invited from MD or PhD graduates for a full-time postdoctoral fellowship focused on epidemiology and mathematical modeling of tuberculosis. The fellow will join a research team based in Brazil, with mentorship from investigators at Yale and Stanford. Research will focus on modeling the transmission and control of tuberculosis in prisons, which are facing a public health crisis in Brazil, and will build upon extensive primary data being collected from ongoing prospective studies there.

The fellowship will be made through Yale or Stanford, but the fellow will be based in Brazil for at least 9 months, with travel to the United States.

The duration of the fellowship will be one year, with potential for extension to a second year. We anticipate that the fellow will serve as a first author paper on one or more research projects with the goal of applying for an independent research position at the end of the fellowship.

The fellowship will begin July 1, 2015.

Qualifications

  • MD or PhD (or expected graduation by July 1, 2015) with relevant background in epidemiology, biostatistics, applied mathematics, ecology, or a related discipline
  • Commitment to a career in global health research
  • Excellent academic record and communication skills
  • Ability to work as part of a highly collaborative team from multiple institutions and countries
  • Experience and fluency in at least one scientific programming language (R, Python, or Matlab preferred, others acceptable)
  • Fluency in written and spoken English
  • U.S.-based applicants must be U.S. citizens or Permanent Residents
  • Foreign scholars must be citizens of a country not considered among high-income countries as defined by the World Bank

Research Team

The postdoctoral fellow will join an experienced team of investigators who are based at Federal University of Grande Dourados in Brazil (Julio Croda, MD, PhD), Yale (Albert Ko, MD) and Stanford (Jason Andrews, MD). This research group currently leads studies focused on the epidemiology, transmission dynamics, and control of tuberculosis in twelve Brazilian prisons and involves interdisciplinary work including conventional and molecular epidemiology, mathematics, environmental science and health policy.

Global Health Equity Scholars Program

The Global Health Equity Scholars (GHES) Program is a mentored research fellowship sponsored by the Fogarty International Center and collaborating institute and centers at the National Institutes of Health. The GHES program brings together a consortium that includes the University of California, Berkeley, Florida International University, Stanford University and Yale University. The main objective of the program is to generate a new and young cadre of global health researchers, educators, and professionals who will be prepared to address the new challenges in global health. The program will provide fellows with outstanding, interdisciplinary education and training in innovative global health research to promote health equity for populations around the world.

Application Procedures

Applicants will first need to apply to the research group and then to the GHES program. Interested applicants should submit a two-page cover letter (including a statement of research interests and relevant experience), CV and contact information for 2 references to jandr@stanford.edu

Applications submitted by October 15, 2014 will be given priority, but should be submitted no later than November 1, 2014.

Applications to the GHES program will be subsequently due: December 1, 2014

Interested applicants should review detailed eligibility criteria and application instructions at ghes.berkeley.edu/application

Contact

For questions about this training opportunity, please contact Jason Andrews, MD jandr@stanford.edu For general questions about the Global Health Equity Scholars program, eligibility and application, please contact: Melaine Delcroix, PhD mdelcroix@berkeley.edu


Research Associate in Modelling and Health Economics, UCL, UK
closing date: 17th Sep 2014, 2300 UK time
more details

Reader/Professor in Infectious Disease Modelling, LSHTM, UK
closing date: 30th Sep 2014
more details