Postdoctoral Research Associate in Mathematical and Statistical Methods for Genome Privacy (Fixed Term)

1000scholars; Cambridge; On-site; GBP 36,000 - 48,000; Full time

Job summary

The University of Cambridge invites applications for a Postdoctoral Research Associate in Mathematical and Statistical Methods for Genome Privacy within the Department of Applied Mathematics and Theoretical Physics. The successful candidate will develop privacy-preserving methods for human pangenome references and collaborate with Dr Gamze Gursoy's group.

Applicants should have a PhD in statistics, mathematics, computer science, computational biology, bioinformatics, genomics, or a related

Qualifications

• PhD in statistics, mathematics, computer science, computational biology, bioinformatics, genomics, or related field.
• Strong programming skills and quantitative research experience.
• Interest in privacy, genomics, and interdisciplinary collaboration.

Responsibilities

• Develop and manage privacy-preserving methods for human pangenome references.
• Collaborate with interdisciplinary team and contribute to publications and conferences.

Skills

Statistics

Graph theory

Optimization

Differential privacy

Algorithms

Computational genomics

Education

PhD in statistics, mathematics, computer science, computational biology, bioinformatics, genomics, or related field

Job description

Postdoctoral Research Associate in Mathematical and Statistical Methods for Genome Privacy

We invite applications for a Postdoctoral Research Associate in Mathematical and Statistical Methods for Genome Privacy in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. The successful candidate will join the research group of Dr Gamze Gursoy and work on developing new mathematical and computational methods for privacy-preserving human pangenome references.

Depending on the successful candidate's interests and background, research directions may include differentially private construction and analysis of pangenome graphs, privacy‑aware graph algorithms and representations, statistical characterization of privacy‑utility trade‑offs, optimization of algorithms for large genomic graphs, and development and benchmarking of methods on human pangenome data. There will be considerable freedom for the successful candidate to help shape the scientific direction of the project.

We are particularly interested in candidates with a strong background in one or more of the following areas:

• Statistics
• Graph theory or graph algorithms
• Mathematical or numerical optimization
• Differential privacy or related areas of data privacy
• Algorithms and theoretical computer science
• Computational genomics, bioinformatics, or pangenomics

We do not expect candidates to be experts in all of these areas.

Applicants with deep expertise in one area and a strong interest in learning and working across the others are strongly encouraged to apply. In particular, we welcome applications from statistics, mathematics, optimization, privacy, or algorithms who are excited about applying their methods to fundamental problems in human genomics, as well as researchers from computational genomics who are interested in developing expertise in privacy.

The project is highly interdisciplinary. The successful candidate will have opportunities to collaborate with Professor Po-Ling Loh in the Statistical Laboratory, Department of Pure Mathematics and Mathematical Statistics (DPMMS), particularly on statistical, optimization, and differential privacy aspects of the work.

The postholder will be expected to develop and manage their own research with guidance from the PI, contribute actively to the intellectual life of the research group, and communicate their work through publications, presentations, seminars, and conferences. They will join a collaborative group working broadly across computational genomics, statistical genetics, privacy‑preserving computation, and trustworthy methods for genomic and biomedical data.

Applicants should have completed, or be close to completing, a PhD in statistics, mathematics, computer science, computational biology, bioinformatics, genomics, or a related field. Evidence of strong quantitative, computational, or methodological research is more important than prior experience in any particular application area. Strong programming skills and an interest in interdisciplinary research are desirable.

The funding requires that candidates must not have been UK residents during the 24 months before the start date, nor be currently employed by a UK organisation or its overseas campuses.

For informal enquiries, please contact Dr Gamze Gursoy at [e-mail].

If you have any queries regarding the application process, please contact [e-mail].