Job posting number: #7185721 (Ref:hh-64042BR)
Posted: October 12, 2023
Application Deadline: Open Until Filled
Position DescriptionWe are seeking a computational expert to join the laboratory of Ting Wu (Harvard Medical School) in their CEGS (NIH/NHGRI) journey to image, analyze, and model the human genome, in its entirety, at super-resolution. Beyond an introductory acclimation to the research program, the successful candidate should very quickly be able to independently carry out, as well as lead, computational studies within the context of the program. A strong sense of collaboration and high levels of self-motivation, creativity, and open-mindedness will be appreciated. Candidates can anticipate working as part of an interdisciplinary team of geneticists, microscopists, chemists, engineers, physicists, and computational experts.
General: The selected individual will be expected to have had extensive computational experience, equivalent to, or exceeding, that of senior postdoctoral fellows in the field of genetics, epigenetics, and 3D genome organization and/or someone who has had significant experience working independently in an industrial setting. The successful candidate will have had experience in Hi-C analysis, genomics, image processing and analysis, both supervised and unsupervised machine/deep learning, parallel computing, and integrative modeling.
Specific: The successful candidate will be addressing long-standing challenges in the field of in situ genome imaging, which falls within the larger field of 3D genome organization. These challenges include: detection of signal over noise (S:N), drift correction, cluster analysis, image analysis for data collected from diffraction-limited and/or single-molecule localization microscopy, pattern recognition within a structurally variable genome, and integrative genome modelling, to mention just a few. The challenges also include bottlenecks in terms of data curation, transfer, and storage, the latter potentially involving tens of Tbs of data being generated per day.
The successful applicant will, therefore, be:
- providing solutions that improve S:N via the development of new imaging strategies and computational pipelines,
- developing de novo strategies for data validation,
- developing de novo tools for image processing (e.g., via denoising, mathematic transformation, automated tracking)
- developing de novo tools for data analysis (e.g., PCA analysis, signal resizing/resampling, alignment, filtering)
- developing new tools for pattern recognition via unsupervised machine learning (e.g., via VAEs, GANs, K-means), including image segmentation, edge detection, and automated tracking.
- developing new pipelines for integrated as well as polymer/biophysics-based modelling,
- developing new tools for predictive modelling (via, e.g., machine/deep learning).
- staying abreast of computational developments elsewhere in and beyond the immediate field, updating the laboratory, and assessment whether the developments are relevant and/or useful to the project.
Basic QualificationsBachelor's degree in biological science or related field. 3+ years relevant experience
Additional Qualifications and Skills
- Applicants should have had extensive training and experience in computational biology, data science, statistics, and/or related fields, including hands-on expertise with machine learning, neural networks, and/or artificial intelligence for building, testing, and evaluating predictive and classification models.
- Applicants must have demonstrated or exceeded the capacity of a senior level postdoctoral fellow in biology, genetics, epigenetics, and/or 3D genome organization.
- Applicants should be proficient in Python, R, C/C++, SQL, Fortran, MATLAB, Julia, etc. and cloud/cluster computing.
- Applicants should have robust experience in supervised (e.g., SVM, RF, XGBoost, LASSO, MLP, U-NET, ResNet, LSTM, and GRUs) and unsupervised (e.g., PCA, K-means, VAEs, GANs) machine/deep learning, Bayesian theory, wave theory, image segmentation and analysis, signal processing (e.g., Convolution, Complex analysis, STFT, CWT, ST, and filtering), transfer learning, etc.
- Applicants should have strong presentation and communication skills both verbal and written.
Additional InformationThis is an in-person position based on our campus in Boston.
The health of our workforce is a priority for Harvard University. With that in mind, we strongly encourage all employees to be up-to-date on CDC-recommended vaccines.
Please note that we are currently conducting a majority of interviews and onboarding remotely and virtually. We appreciate your understanding.
The Harvard Medical School is not able to provide visa sponsorship for this position.
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EEO StatementWe are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions, or any other characteristic protected by law.
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