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Trainee Computer Science Neuroscience Jobs (NOW HIRING)

Dartmouth College

Research Software Engineer

Hanover, NH · On-site

$204.70K/yr

Bachelor's degree in computer science, neuroscience, or related field, or ability to demonstrate equivalent proficiency through a public track record of software engineering contributions (e.g ...

Tacit

Computational Neuroscientist

San Francisco, CA · On-site

$160K - $180K/yr

PhD in neuroscience, biomedical engineering, machine learning, computer science, or related fields (or equivalent industry experience). * Fluency in Python and PyTorch. * Ability to collaborate ...

Mayo Clinic

Research Fellow-Psychiatry

Scottsdale, AZ · On-site

$67.28K - $78.71K/yr

Our departments are committed to the mentorship and professional development of trainees in a ... computer science, neuroscience, electrical engineering, or a closely related field, in addition to ...

Mayo Clinic

Research Fellow-Psychiatry

Scottsdale, AZ · On-site

Our departments are committed to the mentorship and professional development of trainees in a ... computer science, neuroscience, electrical engineering, or a closely related field, in addition to ...

Mayo Clinic

Research Fellow-Psychiatry

Scottsdale, AZ · On-site

Our departments are committed to the mentorship and professional development of trainees in a ... computer science, neuroscience, electrical engineering, or a closely related field, in addition to ...

Mayo Clinic

Research Fellow-Psychiatry

Phoenix, AZ · On-site

Our departments are committed to the mentorship and professional development of trainees in a ... computer science, neuroscience, electrical engineering, or a closely related field, in addition to ...

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How much do trainee computer science neuroscience jobs pay per year?

As of Jun 4, 2026, the average yearly pay for trainee computer science neuroscience in the United States is $43,530.00, according to ZipRecruiter salary data. Most workers in this role earn between $33,000.00 and $51,000.00 per year, depending on experience, location, and employer.
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Research Fellow - Deep Learning

Massgeneralbrigham

Boston, MA • On-site

Full-time

Posted 19 days ago

Job description

Site: Massachusetts Eye and Ear Infirmary


Mass General Brigham relies on a wide range of professionals, including doctors, nurses, business people, tech experts, researchers, and systems analysts to advance our mission. As a not-for-profit, we support patient care, research, teaching, and community service, striving to provide exceptional care. We believe that high-performing teams drive groundbreaking medical discoveries and invite all applicants to join us and experience what it means to be part of Mass General Brigham.



Job Summary

We have an open position for a computer science/machine-learning postdoctoral fellow to work on machine-learning algorithms for automatic diagnosis of dystonia, prediction of the risk for dystonia development, and the efficacy of treatment outcomes. This work will be directly related to the extension of our recently developedDystoniaNet platformand will include brain MRI datasets from patients with dystonia, other movement disorders, and healthy individuals.
The postdoctoral fellow will be part of a multidisciplinary team of neuroscientists, neurologists, laryngologists, and geneticists at Mass Eye and Ear and Mass General Hospital and work at the intersection on the development, testing and implement of DystoniaNet in the clinical setting. This position is best suited for an individual with a broad computer science background interested in understanding and examining critical clinical problems and developing research solutions for their translation to healthcare. The fellow will be highly competitive to pursue future opportunities in either academia or industry (pharma and biotech).


Qualifications

Postdoctoral Fellow in Deep Learning

We have an open position for a computer science/machine-learning postdoctoral fellow to work on machine-learning algorithms for automatic diagnosis of dystonia, prediction of the risk for dystonia development, and the efficacy of treatment outcomes. This work will be directly related to the extension of our recently developedDystoniaNet platformand will include brain MRI datasets from patients with dystonia, other movement disorders, and healthy individuals.

The postdoctoral fellow will be part of a multidisciplinary team of neuroscientists, neurologists, laryngologists, and geneticists at Mass Eye and Ear and Mass General Hospital and work at the intersection on the development, testing and implement of DystoniaNet in the clinical setting. This position is best suited for an individual with a broad computer science background interested in understanding and examining critical clinical problems and developing research solutions for their translation to healthcare. The fellow will be highly competitive to pursue future opportunities in either academia or industry (pharma and biotech).

Responsibilitiesinclude but may not be limited to

  • Experimental data collection and processing
  • Development and refinement of deep learning and other benchmark algorithms for predictive classification of dystonia and other related disorders
  • Clinical translation and implementation of the developed algorithms and interactions with clinicians for their testing
  • Establishment of new and fostering of existing collaborations
  • Participation in the regulatory aspects of clinical translation and patenting
  • Presentation of the results at the scientific meetings and publication of journal articles
  • Mentoring junior staff

Qualifications and Skills

  • PhD or an equivalent degree in computer science, neuroscience, biomedical engineering, or related fields
  • Broad proficiency and experience with supervised and unsupervised machine-learning methods, expertise in building neural network architectures
  • Experience with neuroimaging data processing
  • Advanced programming skills (Python and/or Matlab), including deep learning packages (e.g., TensorFlow or Keras)
  • Knowledge and experience with cloud-based computational platforms (e.g., AWS)
  • Excellent verbal and written communication skills
  • Strong publication record and academic credentials
  • Ability to work effectively both independently and in collaboration with multiple investigators


Additional Job Details (if applicable)


    Remote Type

    Onsite


    Work Location

    243-245 Charles Street


    Scheduled Weekly Hours

    40


    Employee Type

    Regular


    Work Shift

    Day (United States of America)


    EEO Statement:

    5110 Massachusetts Eye and Ear Infirmary is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, national origin, sex, age, gender identity, disability, sexual orientation, military service, genetic information, and/or other status protected under law. We will ensure that all individuals with a disability are provided a reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. To ensure reasonable accommodation for individuals protected by Section 503 of the Rehabilitation Act of 1973, the Vietnam Veteran's Readjustment Act of 1974, and Title I of the Americans with Disabilities Act of 1990, applicants who require accommodation in the job application process may contact Human Resources at (857)-282-7642.


    Mass General Brigham Competency Framework

    At Mass General Brigham, our competency framework defines what effective leadership "looks like" by specifying which behaviors are most critical for successful performance at each job level. The framework is comprised of ten competencies (half People-Focused, half Performance-Focused) and are defined by observable and measurable skills and behaviors that contribute to workplace effectiveness and career success. These competencies are used to evaluate performance, make hiring decisions, identify development needs, mobilize employees across our system, and establish a strong talent pipeline.

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