PhD fellowship in Biostatistics and causal inference for clinical trials at the SMARTbiomed Pioneer centre
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PhD fellowship in Biostatistics and causal inference for clinical trials at the SMARTbiomed Pioneer centre
The Department of Public Health at the University of Copenhagen offers a PhD fellowship in biostatistics with special focus on biostatistical methods for clinical trials. The anticipated hiring date is April 1, 2026, or as soon as possible thereafter. The PhD student is expected to develop and apply statistical methodology for group-sequential trials and will be part of the SMARTbiomed Pioneer centre https://SMARTbiomed.dk/about-SMARTbiomed
Project Description
The PhD student will engage in research to better design and analyze group sequential clinical trials, with a focus on challenges coming from pipeline data and non-standard survival endpoints. Group sequential trials (GST) are a special kind of trial that is increasingly used in both academia and the pharmaceutical industry. GST allow researchers to evaluate accumulating data at preplanned time intervals during a trial, without compromising the validity of the final analysis results. These interim analyses of the data allow for early stopping of a trial in case of a clear effect of the treatment or a clear lack thereof. Such an approach has an ethical advantage, since it helps ensure that individuals included in the trial are not exposed to unsafe, inferior, or ineffective treatments unnecessarily. Also, on average, less resources need to be spent on the trial, and in case the treatment is found to be effective, it can benefit patients outside of the trial sooner. Pipeline data refer to important data about the primary endpoint of the trial that is not yet available at the time of interim analysis for some subjects already included in the trial, but that will be collected and analyzed later in case a decision to stop the trial early is taken as a result of the interim analysis. Pipeline data typically occur with endpoints that are captured at a specific follow-up time after randomization (e.g., 90-day mortality).
The research project originates from two specific case studies. The PhD student will work on providing concrete solutions to specific problems arising from these case studies. The solutions will need to be rigorous and well supported by theoretical arguments, but also pragmatic and fit-for-use in applications within the typical constraints of clinical research. Building on the solutions to the specific challenges of the cases studies, the PhD student is also expected to develop methods and recommendations for the design and analysis of group-sequential trials that would apply broadly, beyond the contexts of the two case studies.
Overall, the aim is to deliver novel methodology and theoretical results, software implementation and tutorials or recommendations to increase the rigor of group sequential trials. The research output should be directly relevant to some of our collaborators and increase the rigor of statistical and causal inference within the context of pivotal clinical trials.
Principal supervisor Associate Professor Paul Blanche, Co-supervisors Professor Thomas Scheike and Senior Statistical Director Henrik Ravn
Start: April 1, 2026
Duration: 3 years as a PhD student
Job description
Your key tasks as a PhD student at SUND are:
The successful applicant for the Ph.D. scholarship will be a statistician with a solid background in mathematical statistics and biostatistics. Accordingly, we require documented skills within theoretical and applied statistics and the ability to program in R. Also, the applicant should be interested in learning to communicate research findings in teaching, conference talks, and by writing scientific papers for international journals. Knowledge of clinical trials will be valued, as well as experience in collaborating with medical researchers. Specific knowledge about group-sequential trials would be appreciated but is not necessary.
It is a prerequisite for the PhD fellowship that the candidate can be and is not already enrolled as a PhD student at the faculty of Health and Medical Sciences, University of Copenhagen.
Place of employment
The place of employment is at the Section of Biostatistics, Department of Public Health, CSS, University of Copenhagen. We offer creative and stimulating working conditions in dynamic and international research environment.
About the Centre: The Pioneer Centre for Statistical and computational Methods for Advanced Research to Transform Biomedicine (SMARTbiomed) will support a critical mass of researchers who focus on method and software development for analysis and inference from massive human data, advancing applications in medicine. The Pioneer Centre for SMARTbiomed will leverage large-scale, multimodal biomedical data combined with focussed innovation in statistical and computational methods including machine learning to advance our understanding, treatment, and prevention of human disease.
The Centre will be anchored at Aarhus University with hub sites at Copenhagen University and University of Oxford. Prof. Naomi Wray, Michael Davys Professor, Department of Psychiatry and Big Data Institute at the University of Oxford is the SMARTbiomed Director. The leadership executive team includes five co-PIs, Professor Chris Holmes, Professor of Biostatistics at the University of Oxford, Professor Cecilia Lindgren, Professor of Genomic Endocrinology & Metabolism, Big Data Institute, University of Oxford, Professor Bjarni Jóhann Vilhjálmsson, Professor of Statistical and Psychiatric Genetics, National Centre for Register-based Research, Aarhus University and Professor Erin Gabriel, Section of Biostatistics at the University of Copenhagen.
SMARTbiomed is funded by the Danish National Research Foundation, the Novo Nordisk Foundation, the Lundbeck Foundation, the Carlsberg Foundation and the Villum Foundation. Funding has been secured to support a 13-year research plan. SMARTbiomed will support researchers who focus on method and software development for analysis of, and inference from, human health-related data, advancing applications in medicine. We will create a vibrant international community of researchers, both virtually and in-person, providing an exciting environment of collaboration to attract early-career researchers from a wide variety of fields, working as a team towards unified goals. Travel between the hubs is encouraged for all centre members.
The core mission of SMARTbiomed is development of statistical and computational methods focussing on causal inference (theme lead: Erin Gabriel), risk prediction (theme lead: Bjarni Vilhjálmsson) and machine learning (theme lead: Chris Holmes). This broad focus is ring-fenced by questions relevant to common complex diseases/disorders (specifically cardiometabolic, brain and reproductive traits), and ring-fenced by big data types that are newly emerging for exploitation (‘omics, longitudinal – usually electronic health record or biobank - and clinical imaging data) Bridging omics and clinical big data within the Centre acknowledges a vision that translation of research advances from omics-based research will be facilitated by close integration with, and knowledge of, clinical data.
Information on the centre can be found at: https://SMARTbiomed.dk/
Terms of employment
The employment as PhD fellow is full time and for 3 years.
It is conditioned upon the applicant’s successful enrollment as a PhD student at the Graduate School at the Faculty of Health and Medical Sciences, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant prior to employment.
The PhD study must be completed in accordance with The Ministerial Order on the PhD programme (2013) and the Faculty’s rules on achieving the degree. Salary, pension and terms of employment are in accordance with the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State. Depending on seniority, the monthly salary begins around 26.755 DKK /approx. 3.567 EUR (April 2018-level) plus pension.
Questions
For specific information about the PhD fellowship, please contact Associate Professor Paul Blanche (pabl@sund.ku.dk).
General information about PhD study at the Faculty of Health and Medical Sciences is available at the Graduate School’s website: https://healthsciences.ku.dk/phd/guidelines/
Application procedure
Submit your application via the link below no later than Jan 22nd., 2026. Applications submitted after this date will not be considered.
The application must be written in English and should consist of the following:
We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the above mentioned requirements.
The further process
After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the hiring committee. All applicants are then immediately notified whether their application has been passed for assessment by an unbiased assessor. Once the assessment work has been completed each applicant has the opportunity to comment on the part of the assessment that relates to the applicant him/herself.
You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/
The applicant will be assessed according to the Ministerial Order no. 242 of 13 March 2012 on the Appointment of Academic Staff at Universities.
The University of Copenhagen wishes to reflect the diversity of society and encourage all qualified candidates to apply regardless of personal background.
Project Description
The PhD student will engage in research to better design and analyze group sequential clinical trials, with a focus on challenges coming from pipeline data and non-standard survival endpoints. Group sequential trials (GST) are a special kind of trial that is increasingly used in both academia and the pharmaceutical industry. GST allow researchers to evaluate accumulating data at preplanned time intervals during a trial, without compromising the validity of the final analysis results. These interim analyses of the data allow for early stopping of a trial in case of a clear effect of the treatment or a clear lack thereof. Such an approach has an ethical advantage, since it helps ensure that individuals included in the trial are not exposed to unsafe, inferior, or ineffective treatments unnecessarily. Also, on average, less resources need to be spent on the trial, and in case the treatment is found to be effective, it can benefit patients outside of the trial sooner. Pipeline data refer to important data about the primary endpoint of the trial that is not yet available at the time of interim analysis for some subjects already included in the trial, but that will be collected and analyzed later in case a decision to stop the trial early is taken as a result of the interim analysis. Pipeline data typically occur with endpoints that are captured at a specific follow-up time after randomization (e.g., 90-day mortality).
The research project originates from two specific case studies. The PhD student will work on providing concrete solutions to specific problems arising from these case studies. The solutions will need to be rigorous and well supported by theoretical arguments, but also pragmatic and fit-for-use in applications within the typical constraints of clinical research. Building on the solutions to the specific challenges of the cases studies, the PhD student is also expected to develop methods and recommendations for the design and analysis of group-sequential trials that would apply broadly, beyond the contexts of the two case studies.
Overall, the aim is to deliver novel methodology and theoretical results, software implementation and tutorials or recommendations to increase the rigor of group sequential trials. The research output should be directly relevant to some of our collaborators and increase the rigor of statistical and causal inference within the context of pivotal clinical trials.
Principal supervisor Associate Professor Paul Blanche, Co-supervisors Professor Thomas Scheike and Senior Statistical Director Henrik Ravn
Start: April 1, 2026
Duration: 3 years as a PhD student
Job description
Your key tasks as a PhD student at SUND are:
- Carry out an independent research project under supervision
- Complete PhD courses or other equivalent education corresponding to approx. 30 ECTS points
- Participate in active research environments including a stay at another research team
- Obtain experience with teaching or other types of dissemination related to your PhD project
- Write a PhD thesis on the grounds of your project
The successful applicant for the Ph.D. scholarship will be a statistician with a solid background in mathematical statistics and biostatistics. Accordingly, we require documented skills within theoretical and applied statistics and the ability to program in R. Also, the applicant should be interested in learning to communicate research findings in teaching, conference talks, and by writing scientific papers for international journals. Knowledge of clinical trials will be valued, as well as experience in collaborating with medical researchers. Specific knowledge about group-sequential trials would be appreciated but is not necessary.
It is a prerequisite for the PhD fellowship that the candidate can be and is not already enrolled as a PhD student at the faculty of Health and Medical Sciences, University of Copenhagen.
Place of employment
The place of employment is at the Section of Biostatistics, Department of Public Health, CSS, University of Copenhagen. We offer creative and stimulating working conditions in dynamic and international research environment.
About the Centre: The Pioneer Centre for Statistical and computational Methods for Advanced Research to Transform Biomedicine (SMARTbiomed) will support a critical mass of researchers who focus on method and software development for analysis and inference from massive human data, advancing applications in medicine. The Pioneer Centre for SMARTbiomed will leverage large-scale, multimodal biomedical data combined with focussed innovation in statistical and computational methods including machine learning to advance our understanding, treatment, and prevention of human disease.
The Centre will be anchored at Aarhus University with hub sites at Copenhagen University and University of Oxford. Prof. Naomi Wray, Michael Davys Professor, Department of Psychiatry and Big Data Institute at the University of Oxford is the SMARTbiomed Director. The leadership executive team includes five co-PIs, Professor Chris Holmes, Professor of Biostatistics at the University of Oxford, Professor Cecilia Lindgren, Professor of Genomic Endocrinology & Metabolism, Big Data Institute, University of Oxford, Professor Bjarni Jóhann Vilhjálmsson, Professor of Statistical and Psychiatric Genetics, National Centre for Register-based Research, Aarhus University and Professor Erin Gabriel, Section of Biostatistics at the University of Copenhagen.
SMARTbiomed is funded by the Danish National Research Foundation, the Novo Nordisk Foundation, the Lundbeck Foundation, the Carlsberg Foundation and the Villum Foundation. Funding has been secured to support a 13-year research plan. SMARTbiomed will support researchers who focus on method and software development for analysis of, and inference from, human health-related data, advancing applications in medicine. We will create a vibrant international community of researchers, both virtually and in-person, providing an exciting environment of collaboration to attract early-career researchers from a wide variety of fields, working as a team towards unified goals. Travel between the hubs is encouraged for all centre members.
The core mission of SMARTbiomed is development of statistical and computational methods focussing on causal inference (theme lead: Erin Gabriel), risk prediction (theme lead: Bjarni Vilhjálmsson) and machine learning (theme lead: Chris Holmes). This broad focus is ring-fenced by questions relevant to common complex diseases/disorders (specifically cardiometabolic, brain and reproductive traits), and ring-fenced by big data types that are newly emerging for exploitation (‘omics, longitudinal – usually electronic health record or biobank - and clinical imaging data) Bridging omics and clinical big data within the Centre acknowledges a vision that translation of research advances from omics-based research will be facilitated by close integration with, and knowledge of, clinical data.
Information on the centre can be found at: https://SMARTbiomed.dk/
Terms of employment
The employment as PhD fellow is full time and for 3 years.
It is conditioned upon the applicant’s successful enrollment as a PhD student at the Graduate School at the Faculty of Health and Medical Sciences, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant prior to employment.
The PhD study must be completed in accordance with The Ministerial Order on the PhD programme (2013) and the Faculty’s rules on achieving the degree. Salary, pension and terms of employment are in accordance with the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State. Depending on seniority, the monthly salary begins around 26.755 DKK /approx. 3.567 EUR (April 2018-level) plus pension.
Questions
For specific information about the PhD fellowship, please contact Associate Professor Paul Blanche (pabl@sund.ku.dk).
General information about PhD study at the Faculty of Health and Medical Sciences is available at the Graduate School’s website: https://healthsciences.ku.dk/phd/guidelines/
Application procedure
Submit your application via the link below no later than Jan 22nd., 2026. Applications submitted after this date will not be considered.
The application must be written in English and should consist of the following:
- Motivation letter of application (max 2 pages). Please briefly describe your previous research experiences and interests and what attracts you to the position in terms of both the topic and the academic work.
- Curriculum vitae detailing education, work, research and teaching experiences, conference presentations, publication record, language skills and other skills relevant for the position
- Documentation of knowledge about mathematical statistics, biostatistics, clinical trials and experience in collaborating with medical researchers. A list of methods you have worked with and in which context would be appreciated.
- Official transcripts of examination results.
- A certified/signed copy of degree certificate(s).
- Contact information for two reference persons.
We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the above mentioned requirements.
The further process
After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the hiring committee. All applicants are then immediately notified whether their application has been passed for assessment by an unbiased assessor. Once the assessment work has been completed each applicant has the opportunity to comment on the part of the assessment that relates to the applicant him/herself.
You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/
The applicant will be assessed according to the Ministerial Order no. 242 of 13 March 2012 on the Appointment of Academic Staff at Universities.
The University of Copenhagen wishes to reflect the diversity of society and encourage all qualified candidates to apply regardless of personal background.