Applied Statistics, Biometrics/biostatistics, Data analysis/processing
The Department of Biostatistics at the Harvard T.H. Chan School of Public Health has an immediate opening in CBAR for a Ph.D.-level Research Associate or Research Scientist statistician to work in the International Maternal Pediatric Adolescent AIDS Clinical Trials Network (IMPAACT) and other infectious diseases projects.
IMPAACT is a large NIH-funded collaborative clinical trials network with an international research agenda focused on the treatment, cure, and prevention of HIV infection and associated co-morbidities, particularly tuberculosis and viruses other than HIV, in children, adolescents, and pregnant or breastfeeding women. Successful applicant will join over 100 statisticians, epidemiologists and research staff collaborating on the design, monitoring, analysis and reporting of Phase I through Phase IV clinical trials, diagnostic studies and observational studies.
Applicants should have the potential to take a leadership role in IMPAACT and other infectious diseases projects, and the ability to: work as part of collaborative teams in designing, conducting and reporting results from clinical trials and observational studies; provide the statistical expertise for and lead the statistical work on studies including mentoring and supervising other staff; and develop a self-initiated research agenda related to their HIV and infectious diseases research projects. The position may include statistical methods research for someone with an appropriate background. This position provides an opportunity to be a leader in a new generation of statistician-trialists. This is a term appointment, renewable upon mutual consent.
The full description of the position, and application can be found below:
Candidates should have a doctoral degree in biostatistics or statistics with interest in clinical trials and diagnostic studies. The Research Associate position is available for new doctoral graduates. The Research Scientist appointment requires at least two years of clinical trials or related applied experience beyond the doctoral degree.
A successful candidate will demonstrate the ability to provide statistical expertise and leadership in collaborative and methodological research, and successfully work independently in the design, conduct, monitoring and analysis of clinical trials and observational studies.
Strong SAS or R programming with simulation abilities
Strong written and oral communication skills
Ability to effectively work as part of a collaborative team
Ability to mentor and supervise other staff
This is a term appointment, renewable upon mutual consent.
To learn more about CBAR research, please visit: www.hsph.harvard.edu/cbar. For additional inquiries, please email the CBAR Administrative team at email@example.com
Applicants must apply through the Harvard ARieS application site by submitting a CV, cover letter, transcripts and names of three references whom we can contact via email. Please also complete the Harvard University application form here.
Additional Salary Information: This position is an appointment that is reviewed annually by the office of Faculty Affairs. CBAR offers a competitive salary package set by the department, as well as benefits and discounts through the university. To learn more about Harvard''s benefit plans, visit: http://hr.harvard.edu/jobs-total-rewards
About Harvard T.H. Chan School of Public Health - Center for Biostatistics in AIDS Research
Founded in 1995, The Center for Biostatistics in AIDS Research (CBAR) within the Department of Biostatistics at the Harvard T.H. Chan School of Public Health is responsible for the design, monitoring, and analysis of numerous federally funded clinical trials of treatment and prevention trials for HIV infection and other infectious diseases, both within the US and internationally.
Our current re...search focuses on methods for patient-focused outcome measures that integrate efficacy and safety outcomes, for competing risks data, for personalizing treatment, and for comparison of technologies (e.g., point of care versus gold standard technologies). We also have a particular focus on the development and application of sophisticated epidemiological models for evaluating treatment benefits accounting for confounding by indication, such as the use of marginal structural models, mediation analysis, and other causal inference approaches.