The Scientific Computing Associate II (SCA II) position represents an alternative to the traditional postdoc and provides an ideal environment to establish a career in computational research or software engineering. The position aims at developing qualifications and experience in computational research and professional software engineering in a research environment that enables the candidate to pursue her/his future career in science or industry.
We are seeking a talented and motivated computational scientist to develop solutions that enable robust and automated processing of large-scale, high-dimensional microscopy datasets that are produced by next-generation light-sheet microscopes for ultrafast high-resolution volumetric imaging of living organisms. These microscopes are being developed in the Keller lab and enable fundamentally new measurements in the imaging of whole-animal development as well as in the functional imaging of the brain.
The purpose of the computational solutions is to convert these massive datasets into biologically meaningful representations that can be interpreted and analyzed by domain experts.
The candidate will work in a close collaboration between the Computational Methods Group in Scientific Computing led by Stephan Preibisch and the Keller lab. The candidate will have access to the full spectrum of computational resources at Janelia and will be embedded in a highly collaborative peer group that is focused on large-scale computational methods development.
The SCA II position is a time-limited appointment for 12 to 24 months, with discretionary renewal for a final 12-month term.
The candidate will:
- Develop algorithmic and software solutions for data reconstruction of up to petabyte-sized image data employing state-of-the-art computational frameworks and distributed processing schemes. This includes solutions aimed at, for example, efficient data handling, real-time compression, or alignment and fusion of multi-tile & multi-view data.
- Adapt or advance strategies and implementations for data analysis of cellular dynamics in developing embryos and neuronal activity measurements across entire brains. This includes, for example:
- Machine learning to facilitate signal extraction and object detection/segmentation
- Dimensionality reduction (PCA, t-SNE, Autoencoders …)
- Data analysis schemes for data-driven, closed-loop experimental designs
- A degree in computational sciences (ideally M.Sc. or Ph.D)
- Experience in algorithm development for big data, ideally in image analysis
- Experience in solving complex problems independently
- Experience with data analysis, e.g. machine learning, dimensionality reduction, …
- Experience with common programming languages: e.g. Java, Python, C++/C#, …
- Experience working with common data formats, e.g. TIFF, HDF5, N5, KLB, zarr, JSON.
- Good communication skills, comfortable working collaboratively in a team environment.
HHMI is an Equal Opportunity Employer.