The successful applicant will work under direct supervision from the PI in designing, testing, and validating algorithms and models for data analysis, providing data visualization of analyzed results for various ongoing life sciences projects. The successful applicant will use professional bioinformatics, computer science, and data science concepts as well as computational research and development principles, and assist in additional analyses as needed to achieve research objectives. This role develops and optimizes a variety of computational, data science, and CI research tools and components. The successful applicant forms research on current and future HPC, data, and CI technologies, hardware and software projects. In addition, this role works on algorithm development, optimization, programming, performance analysis and / or benchmarking assignments of moderate scope where the tasks involve knowledge of data science and bioinformatics.
Specific Job Responsibilities:
Utilizes standard software tools such as Tensorflow, Keras, PyTorch, and/or Theano, in Python to develop and implement deep-learning and related methods to analyze cardiovascular imaging and associated metadata.
Plans, designs, develops, modifies, debugs, deploys and evaluates supervised and unsupervised machine learning and other computational and data science techniques (including but not limited to neural networks, discriminant analysis, tree-based methods, boosting, random forests, and support vector machines) to imaging data and associated clinical metadata for tasks such as classification and regression. Analyzes existing software, scientific codes, data science / analytics codes / algorithms, and HPC related hardware and software or works to formulate logic for new algorithms. May work with domain scientists where applicable to perform these responsibilities and uses domain science knowledge where relevant.
Utilizes existing algorithms, techniques, and statistical methodologies (such as [incremental] principal/independent component analysis, t-SNE, and data augmentation techniques) to conduct moderately complex data analysis of machine learning results.
Participates and contributes to HPC / data science / CI research proposals in collaboration with other researchers and Principal Investigator (PI) s from the organization.
Develops and/or implements Python-based code for mining, labeling, and ‘cleaning’ data of several types (e.g. semi-structured text, images, vectors/matrices)
Develops and maintains architecture for data storage
PhD in bioengineering, computer science, or related computational field, or ten or more years of experience in the relevant specialization
Fluency in Python
Working knowledge of R and bioinformatics pipelines
Expertise in machine learning techniques such as convolutional neural networks, recursive neural networks, LSTMs, random forests
Expertise in computer vision techniques
Interest and experience in applying computational analysis to life sciences data
Demonstrate high integrity and professionalism to work with patient data in a HIPAA-compliant and morally and ethically responsible manner
Self-motivated, able to learn quickly, meet deadlines and demonstrate strong organizational and problem-solving skills.
Knowledge of application and data security concepts.
Intermediate knowledge of HPC / data science / CI.
Demonstrated ability to contribute research and technical content to grant proposals.
Demonstrated effective communication and interpersonal skills.
Demonstrated ability to communicate technical information to technical and non-technical personnel at various levels in the organization and to external research and education audiences.
Proven skills and experience in independently resolving broad computing / data / CI problems using introductory and / or intermediate principles.
Self-motivated and works independently and as part of a team. Able to learn effectively and meet deadlines.
Demonstrated experience and ability to collaborate effectively with all levels of staff; technical, students, faculty and administrators
Advanced skills, and demonstrated experience associated with one or more of the following: HPC hardware and software power and performance analysis and research, design, modification, Implementation and deployment of HPC or data science or CI applications and tools.
Demonstrated broad experience in one or more of the following: optimizing, benchmarking, HPC performance and power modeling, analyzing hardware, software, and applications for HPC / data / CI.
Experience in data science, specifically with cutting-edge machine learning approaches to data analysis.(1 year minimum; 2-3 years preferred)
Demonstrated ability to regularly interface with management.
Proven ability to understand research computing / data / CI needs, mapping use cases to requirements and how systems / software / infrastructure can support those needs and meet the requirements.
Demonstrated ability to develop and implement such solutions.
Proven ability to successfully work on multiple concurrent projects.
UC San Francisco seeks candidates whose experience, teaching, research, or community service has prepared them to contribute to our commitment to diversity and excellence.
The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status.
The University of California, San Francisco (UCSF) is a leading university dedicated to promoting health worldwide through advanced biomedical research, graduate-level education in the life sciences and health professions, and high-quality patient care. It is the only UC campus in the 10-campus system dedicated exclusively to the health sciences.