Scientific Computing Associate - Data-Driven Equation Discovery
About The Position
The Scientific Computing Associate (SCA) position represents an alternative to traditional scientific roles (e.g. 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 their future career in science or industry. The SCA position is a time-limited appointment for 24 months, with discretionary renewal for a final 12-month term (maximally 36 months in total.) We are seeking a talented and motivated candidate with experience in computational modeling (ideally with expertise in implementing computational schemes for ODE and/or PDE based models) and machine learning methods.The goal is to discover governing equations from experimental data to generate mathematical models of cellular signaling dynamics. You will help design algorithms for data-driven model discovery, test proposed algorithms in computational simulations, and collaborate closely with biologists to propose experimental tests of models in an iterative loop. On this project, you will be working in Scientific Computing with Michael Innerberger, reporting to Stephan Preibisch and collaborating closely with the Sgro lab, as well as the Beyene lab and others at Janelia who generate imaging data of cellular signaling. This project offers a rich, collaborative environment with mentorship from experts in both biological imaging and machine learning. Knowledge of fluid dynamics and biochemical reaction dynamics is a bonus, but not necessary.
Requirements
- A Ph.D. in computer science, mathematics, or a related field with relevant expertise
- Experience with computational modeling, ideally with implementing computational schemes for ODE and / or PDE based models
- Strong communication skills
- Enthusiasm to learn new skills, apply your expertise creatively to new projects, and work collaboratively within and across labs
Nice To Haves
- Ideally experience with machine learning methods for identifying governing equations from experimental observations (e.g., https://github.com/dynamicslab/pysindy )
- Knowledge of fluid dynamics and biochemical reaction dynamics is a bonus, but not necessary.
Responsibilities
- Apply and extend methods for discovering governing equations from experimental data
- Design and run simulations to test proposed equations
- Explore and analyze microscopy data with data science and machine learning methods
- Collaborate closely with experimentalists to test proposed mathematical models
- Collaborate and bring your expertise to larger projects across labs at Janelia
- Assist in preparing publications sharing your research findings
Benefits
- A competitive compensation package, with comprehensive health and welfare benefits
- The opportunity to engage with world-class researchers, software engineers and AI/ML experts, contribute to impactful science, and be part of a dynamic community committed to advancing humanity’s understanding of fundamental scientific questions.
- Experience building and managing scientific collaborations
- A supportive training environment with ample opportunities to participate in scientific workshops and conferences
- Amenities that enhance work-life balance, such as on-site childcare, free gyms, available on-campus housing, social and dining spaces, and convenient shuttle bus service to Janelia from the Washington D.C. metro area.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
Number of Employees
5,001-10,000 employees
