Job Available / Help Wanted / Professional Vacancy

Postdoctoral Fellowship in Computational Biology:
Modeling and Simulation of Multi-Cellular Self-Organization during Tissue Regeneration

Job Description:

The successful candidate will carry out cutting-edge research in multi-scale modelling of cell behavior during injury response in a tissue context. The position is part of an interdisciplinary team at the Centre for Computational Biology (CCB) at the Duke-NUS Medical School Singapore, and at the Biosystems and Micromechanics (BioSyM) group of the Singapore-MIT Alliance for Research and Technology (SMART), under the supervision of Prof. Lisa Tucker-Kellogg and Prof. Peter So. This position will be based in Singapore, with the possibility of travel to MIT (Cambridge, Massachusetts) as part of the collaboration.

Specific goals will be to develop a computational simulation that recapitulates image-based evidence about stem cell division; migration of new cells toward sites of injury; appearance of differentiation; and cell-cell interaction, revolving around the basement membrane as an organizing structure of the tissue. Further development of the project would be adapted according to the interests of the candidate, and might include fibrotic/fibrogenic processes with evidence from SHG imaging of collagen.

This type of research uses biological evidence to refine and improve computational models, and then uses computational models to better interpret tissue imaging data, in a cycle of mutual benefit. The job would likely focus on a single tissue type (e.g., muscle), but the scientist would provide technical assistance to colleagues who focus on other tissue types (e.g., liver, skin). The majority of the work will be computational modelling and simulation, so candidates are expected to enjoy programming as their primary workload. MATLAB is frequently used but other choices are welcome.

Much of the biological data for this work would come from our experimental collaborators, but this job also requires gathering previously published information through independent reading. Candidates are expected to be comfortable reading primary research articles in biological journals.

Qualifications:

1. Interdisciplinary Experience.
Candidates must have experience applying computational or quantitative approaches to biological processes, whether at the molecular, cellular, or organ scale.
2. Modelling Experience.
Candidates must have experience using computational modelling as part of their research.
3. History of Successful Research.
Candidates must be able to demonstrate a track record of successful research. Normally this is indicated by peer-reviewed publications.
4. Programming Ability.
Candidates must be fluent programmers with instincts for modular design and logical debugging.
5. Reading Biology.
The job will require searching the biological literature, reading primary research articles from biological journals, and the ability to critique experimental evidence.
6. Doctorate.
Candidates must hold a doctoral degree (PhD, DSc, etc) or be near completion of a doctoral degree, from a reputable university. Relevant fields of study include computational biology, biophysics, computer science, bioengineering, applied mathematics, quantitative biology, biomechanics, computational physiology, automated pathology, or neuroimaging. Candidates wishing to substitute professional experience instead of a doctoral degree will be considered on a case-by-case basis.
7. Communication and Organization.
Candidates must be able to communicate scientific information precisely and persuasively using written text, graphs, or conversation. Candidates must have good technical and organizational skills.
Optional: Geometry.
Preference will be given to applicants who have familiarity working with discretized geometric representations such as meshes, splines, finite element nodes, convex hulls, or other computational formalisms to describe regions of space. fMRI data analysis is an example. Formal education in computational geometry is welcome but not expected.
Optional: Image Sciences.
Familiarity with image analysis, machine vision, or computer graphics is welcome but not expected. Familiarity with optics, fluorescence microscopy, or electron microscopy is welcome but not expected.

How to Apply:

Please send a cover letter and a full CV (which is a detailed, multi-page resume) via email to Prof. Lisa Tucker-Kellogg (lisa.tucker-kellogg@duke-nus.edu.sg) with copy (CC) to Prof. Peter So (ptso@mit.edu). Note that a single-page business resume is not sufficient for us to understand the extent of your education, experience, and research.

All applications will first be screened for the qualifications listed above. To ensure we consider each application fairly, candidates are invited to tell us explicitly what they have done that fulfils each of the required qualifications. This explanation could be part of the cover letter or in a separate bullet list. Applicants are invited but not required to submit the Duke-NUS employment application form.

Applications will be accepted from 26 Jan 2015 until the position is filled. Singapore citizens and permanent residents are particularly encouraged to apply. We regret that only short-listed applicants will be contacted for follow-up. Any applicant not selected for this position will have his or her application held (confidentially) for 6 months, in case another position becomes available.

Salary and benefits will be determined according to university guidelines.

Keywords:

Mathematical Modelling, Cellular Behaviour, Wound healing, Injury Response, Biophysics, Image analysis, Collective Migration, Self-Organization, Complexity Science, Self-Assembly of Tissue, Agent-Based Models, Finite Element Method, Shape Representation, Differential Equations, Cell Morphology