Job Details

Substance Use and Machine Learning Research Assistant
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Position Number: 5965135
Location: Boston, MA
Position Type: Laboratory and Research

Substance Use and Machine Learning Research Assistant

About the Opportunity

About the Institute for Experiential AI and Northeastern University
Do you want to be part of an exciting new Institute focused on combining human and machine intelligence into working AI solutions?

We have launched a pioneering research and innovation hub in AI-one that will shape the way humans and machines collaborate for decades to come. Led by Dr. Usama Fayyad, the Institute for Experiential AI is built around the challenges and opportunities made possible by human- machine collaboration. The Institute provides a framework to design, implement, and scale AI- driven technologies in ways that make a true difference to society. Our ability to respond to the opportunities afforded to society will depend on training and building a workforce that is AI- capable and prosperous.

Founded in 1898, Northeastern is a global research university and the recognized leader in experience-driven lifelong learning. Our world-renowned experiential approach empowers our students, faculty, alumni, and partners to create impact far beyond the confines of discipline, degree, and campus.

The Culture
Here at the Institute for Experiential AI (EAI) we are committed to the highest standards in all that we do. Working at the EAI offers opportunities, an environment, and a culture that just aren't found together anywhere else. This is the right place for you if you're curious, motivated by the future of technology, and want to be part of a unique and diverse community that works on high- impact research, educational, business, and societal problems.

Position Summary
The Substance Use and Machine Learning Research Assistant will report to Annika Marie Schoene, Research Scientist at EAI and collaborate with Scientists and domain experts from McLeod Centers for Wellbeing and Silvio Amir's group at Khoury College, Northeastern University. Responsibilities will include applying Natural Language Processing methods that automatically extract Social Drivers of Health, write python code for data analysis, and build novel methods using standard Machine Learning libraries (e.g.: HuggingFace and PyTorch). There will be opportunities to co-author publications and contribute to grant proposals. Tentative start date: February 2025 for 6 months with possibilities of renewal. This work will contribute towards the understanding of Social Drivers that impact health outcomes in people who undergo Medication Assisted Treatment for Opioid Use Disorders.

Qualifications:

MS in Computer Science or related fields or currently enrolled in a master's program. A minimum of 3-4 years of experience working with data pipelines and data analysis.
Proficiency in Python and R. Experienced with Unix and remote computing clusters.
Have demonstrable expertise in natural language processing, especially textual data,
Electronic Health Records and have a good grasp of statistics.
Experience with machine learning, graph leaning, or transformers (hands on work)
Familiarity with Social Drivers of Health (understanding health inequities and outcomes).
Experience working with PyTorch and HuggingFace is a bonus.
An ability to write clean and well-documented code and work with GitHub repositories.
Excellent organizational skills including attention to detail and multitasking.
An ability to troubleshoot and problem-solve in response to challenges, especially, in an unfamiliar domain.
Experience with AWS is a plus but not required

Values & Abilities:

Excellent written and verbal communication skills and ability to communicate effectively with a variety of different stakeholders from various academic backgrounds.
Respect for diversity and the importance of interdisciplinary teams.
Self-starter and innovative thinker and a team-player who can collaborate effectively in a university setting.
Open-minded, assertive, and professional when collaborating and working within our team and with other groups within Northeastern University, Harvard Medical School, and University of Virginia.

Key Responsibilities:
The Bioinformatics Co-Op will be responsible for a wide variety of data-oriented tasks, including:

Building ETL pipelines for genomics/transcriptomics data from public databases and processing them with publicly available codes in python and R.
Documenting the entire process and all the codes generated and maintaining structured and regular commits in a Github repository.
Implementing machine learning models and automating the processing of input data.
Write reports/prepare slide decks describing work performed.
Contribute to scientific manuscripts and grant proposals where appropriate.

Position Type

Temporary

Additional Information

Northeastern University considers factors such as candidate work experience, education and skills when extending an offer.

Northeastern has a comprehensive benefits package for benefit eligible employees. This includes medical, vision, dental, paid time off, tuition assistance, wellness & life, retirement- as well as commuting & transportation. Visit https://hr.northeastern.edu/benefits/ for more information.

Northeastern University is an equal opportunity employer, seeking to recruit and support a broadly diverse community of faculty and staff. Northeastern values and celebrates diversity in all its forms and strives to foster an inclusive culture built on respect that affirms inter-group relations and builds cohesion.

All qualified applicants are encouraged to apply and will receive consideration for employment without regard to race, religion, color, national origin, age, sex, sexual orientation, disability status, or any other characteristic protected by applicable law.

To learn more about Northeastern University's commitment and support of diversity and inclusion, please see www.northeastern.edu/diversity.

To apply, visit https://northeastern.wd1.myworkdayjobs.com/en-US/careers/job/Boston-MA-Main-Campus/Substance-Use-and-Machine-Learning-Research-Assistant_R131422