What We Provide:

• A competitive compensation package, with comprehensive health and welfare benefits.
• The opportunity to collaborate with skilled scientists and software engineers and work alongside computational and experimental enthusiasts.
• The ability to work as an independent scientist.
• An exciting and inspiring work environment at HHMI Janelia

What You'll Do:

We are seeking a talented and motivated candidate with machine learning experience to develop a deep learning classifier to identify the ancestors of genes of unknown origin, which is sometimes called the dark matter of the genome. On this project, you will be working in Scientific Computing Software reporting to Stephan Preibisch and collaborate with the Stern Lab . You will receive additional mentorship and guidance from Dr. Srini Turaga. Knowledge of genome and protein structure is a plus, but not necessary.

Many functional elements of the genome evolved so rapidly that their ancestral DNA sequences (remote homologs) can no longer be identified using standard DNA sequence similarity methods (e.g., BLAST). Many genes that parasites introduce into hosts, such as the so-called bicycle genes that small insects called aphids use to control plant physiology and development, are in this category. The Stern lab showed that remote homologs of bicycle genes can be found using a linear classifier that exploits gene structure features (specific DNA sequence elements within a gene such as exon size, number, phase, etc.) rather than only gene sequences. However, they also found that gene structure is evolving within the bicycle gene family and that the classifier loses power with more distantly related species.

Cells transcribe and translate gene sequences into proteins that carry out cellular functions and protein structure tends to be more highly conserved than the underlying gene sequence. A recent break-through in artificial intelligence, AlphaFold, which was recently awarded a Nobel prize, now allows researchers to predict protein structures of any gene. There is now the opportunity to use this abundant protein structure information together with gene structure and sequence information to search for remote homologs.

You will build a deep learning classifier that will exploit (1) genome sequence, (2) gene structure, and (3) predicted protein structure simultaneously both to identify remote homologs of bicycle genes and genes of unknown function across the tree of life.

If the classifier proves generally useful, there is an option to apply for support to develop it into a user-friendly and developer-friendly tool supported by the Janelia Open Science Software Initiative.

What You Bring:

• A degree in computational sciences or equivalent (ideally M.Sc. or Ph.D.)
• Experience in machine learning (ML).
• Experience with the Python programming language
• Experience with PyTorch, JAX etc.
• Experience in solving complex problems independently.
• Good communication skills, comfortable working collaboratively in a team environment.
• Knowledge of genome and protein structure and experience in genomics is a plus.
• Experience with AlphaFold is a plus.

Please include a cover letter with your application.

Physical Requirements:

Remaining in a normal seated or standing position for extended periods of time; reaching and grasping by extending hand(s) or arm(s); dexterity to manipulate objects with fingers, for example using a keyboard; communication skills using the spoken word; ability to see and hear within normal parameters; ability to move about workspace. The position requires mobility, including the ability to move materials weighing up to several pounds (such as a laptop computer or tablet).

Persons with disabilities may be able to perform the essential duties of this position with reasonable accommodation. Requests for reasonable accommodation will be evaluated on an individual basis.

Please Note:

This job description sets forth the job's principal duties, responsibilities, and requirements; it should not be construed as an exhaustive statement, however. Unless they begin with the word "may," the Essential Duties and Responsibilities described above are "essential functions" of the job, as defined by the Americans with Disabilities Act.

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Compensation:

A Scientific Computing Associate is compensated at a rate of $83,000.00 annually at HHMI's Janelia Research Campus.

HHMI's salary structure is developed based on relevant job market data. HHMI considers a candidate's education, previous experiences, knowledge, skills and abilities, as well as internal equity when making job offers.

Compensation and Benefits

Our employees are compensated from a total rewards perspective in many ways for their contributions to our mission, including competitive pay, exceptional health benefits, retirement plans, time off, and a range of recognition and wellness programs. Visit our Benefits at HHMI site to learn more.

HHMI is an Equal Opportunity Employer