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Machine Learning Neuroscience Jobs in Massachusetts

Neurable, INC

Machine Learning

Cambridge, MA

... neuroscience, enables people to control hardware and software in real-time with their brain via a ... We are currently looking for a Machine Learning Scientist/Researcher to join our team. We would ...

Neurable, INC

Machine Learning

Cambridge, MA · On-site

... neuroscience, enables people to control hardware and software in real-time with their brain via a ... We are currently looking for a Machine Learning Scientist/Researcher to join our team. We would ...

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Machine Learning Neuroscience information

What are the key skills and qualifications needed to thrive in the machine learning neuroscience position, and why are they important?

To thrive in a Machine Learning Neuroscience role, you need a solid background in neuroscience, advanced machine learning methods, statistical analysis, and preferably a graduate degree in a related field. Experience using programming languages such as Python or MATLAB, along with tools like TensorFlow, PyTorch, and neuroimaging platforms, is highly desirable. Strong analytical thinking, effective communication, and the ability to work collaboratively across interdisciplinary teams are vital soft skills. These competencies enable professionals to develop impactful models, interpret complex brain data, and drive innovative research or clinical applications in neuroscience.

What is a machine learning neuroscience?

A Machine Learning Neuroscience job involves using machine learning techniques to analyze and model neural data, helping to understand brain function or improve neurotechnology. Professionals in this field work at the intersection of artificial intelligence, neuroscience, and data science, often developing algorithms to interpret neural signals or enhance brain-computer interfaces. Roles can be found in academia, healthcare, and tech industries, contributing to research, diagnosis, or neuroadaptive systems. Strong skills in programming, statistics, and neuroscience fundamentals are typically required.

What are the typical daily tasks and team dynamics for someone working in a machine learning neuroscience role?

In a Machine Learning Neuroscience position, your daily activities might include designing and running algorithms on neurological datasets, interpreting results, refining models, and collaborating with neuroscientists and clinicians. You’ll often work closely with cross-functional teams, contributing technical expertise to research studies or healthcare projects. Regular team meetings, data discussions, and collaborative problem-solving are a central part of the work environment. This collaborative structure fosters innovative ideas and ensures that machine learning approaches are well-suited to real-world neuroscience challenges.

Is machine learning used in neuroscience?

Machine learning neuroscience involves applying machine learning techniques to analyze neural data, model brain functions, and develop brain-computer interfaces. Professionals in this field often use tools like Python, TensorFlow, and neural imaging data to advance understanding of the brain. It is a growing area that combines expertise in both neuroscience and machine learning algorithms.

What are the most commonly searched types of Machine Learning Neuroscience jobs in Massachusetts?

The most popular types of Machine Learning Neuroscience jobs in Massachusetts are:

What are popular job titles related to Machine Learning Neuroscience jobs in Massachusetts?

For Machine Learning Neuroscience jobs in Massachusetts, the most frequently searched job titles are:

What job categories do people searching Machine Learning Neuroscience jobs in Massachusetts look for?

The top searched job categories for Machine Learning Neuroscience jobs in Massachusetts are:

Machine Learning Neuroscience jobs near you
Infographic showing various Machine Learning Neuroscience job openings in Massachusetts as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 20% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Fellow in AI/ML for NeuroAI and Computational Neurobiology

Harvard University

Cambridge, MA • On-site

$54K - $73K/yr

Full-time

Re-posted 25 days ago

Harvard University rating

8.5

Company rating: 8.5 out of 10

Based on 12 frontline employees who took The Breakroom Quiz

80th of 618 rated colleges and universities

Job description

Position
Details
Title
Fellow in AI/ML for NeuroAI and Computational Neurobiology
School
Faculty of Arts and Sciences
Department/Area
Kempner Institute for the Study of Natural and Artificial Intelligence
Position Description
The Kempner Institute at Harvard University seeks early-career researchers to help shape the future of NeuroAI as Kempner AI Fellows. We are looking for candidates with strong foundations in modern machine learning and the ambition to build brain foundation models and other AI systems that advance our understanding of neural activity, brain circuits, and biologically grounded intelligence.
We seek candidates with strong technical preparation in modern AI/ML, a demonstrated record of research accomplishment, and an interest in contributing to ambitious research at the intersection of machine learning, neuroscience, and computational biology. This role centers on computational neurobiology and the use of modern AI/ML methods to model brain circuits and neural activity. In particular, fellows will help develop brain foundation models that predict patterns of neural activity from large-scale, multi-regional recordings.
We are especially interested in candidates with experience in computational neurobiology or neural data analysis, and expertise in one or more of the following areas:
  • foundation model training, evaluation, and adaptation
  • time-series modeling
  • transformers, autoencoders, and dynamical systems models
  • modeling brain circuits and neural activity from large-scale recordings
  • large-scale scientific applications of AI/ML, including in the life sciences

AI Fellows will work closely with one another and with Kempner faculty, researchers, and students on foundational machine learning and neuroscience-informed scientific applications. The position is particularly well-suited to candidates eager to apply their technical expertise in foundation models to important questions in neuroscience and biological intelligence.
Appointment Terms
  • Fellows will conduct research under the direction of a Kempner Institute investigator.
  • Fellows are appointed for a one-year term; reappointment may be possible for up to three consecutive years.
  • Due to the importance of in-person mentoring, this position is based on campus, full-time, at Harvard University. Remote work for this position is not possible.

Basic Qualifications
  • Bachelor's or master's degree in computer science, statistics, electrical engineering, applied mathematics, computational neuroscience, computational biology, neurobiology, physics, or a related quantitative field required by the expected start date
  • Strong technical background in modern AI/ML, including deep learning and hands-on experience with frameworks such as PyTorch or JAX
  • Demonstrated research productivity, including publications in venues such as ICML, ICLR, NeurIPS, COSYNE, CCN, or similar, and/or substantial open-source research contributions
  • Demonstrated experience implementing, training, evaluating, or fine-tuning modern machine learning models
  • Strong programming skills in Python and experience building and maintaining research code
  • Demonstrated ability to use modern AI-assisted and agentic coding tools effectively, such as Claude Code, Codex, or similar systems, in research and development workflows
  • Experience in computational neurobiology, neural data analysis, or modeling neural activity from large-scale recordings
  • Ability to work effectively in a collaborative research environment and communicate technical work clearly

Additional Qualifications
  • Experience with foundation model training, post-training, adaptation, or evaluation
  • Experience with time-series modeling
  • Experience with transformers, autoencoders, dynamical systems models, or related approaches for sequential or neural data
  • Experience modeling brain circuits and neural activity from large-scale, multi-regional recordings
  • Experience with large-scale datasets, distributed training, or high-performance computing environments
  • Expertise in neuroscience, biologically grounded intelligence, and scientific applications of AI/ML

Special Instructions
Please submit the following items in PDF format no later than 11:59pm EST Monday, June 1, 2026:
  1. CV.
  2. A research statement of no more than 2 pages describing your experience using modern AI/ML to model neural activity, brain circuits, or other neuroscience data. Please be specific about your individual contributions.
  3. References - 1-2 required
    • Please give the emails of up to 2 individuals who can describe your work and your potential for future discoveries.
    • Referees will be contacted to submit the letters directly to the Kempner Institute.
    • The application will not be considered complete until all letters have been received.
  4. Transcripts (Undergraduate and/or Master's)

Candidates selected for further consideration will be asked to submit a short video presentation reviewing their past work; additional details will be provided at that stage. Following review of the videos, a subset of candidates will be invited to interview with members of the selection committee via zoom.
Applications received after the deadline will be reviewed on a rolling basis if positions remain available.
We anticipate a start date of September 15, 2026.
Contact Information
Molly Marshall
Contact Email
Kempnerinstitute@harvard.edu
Salary Range
Expected annual salary is $54,600 for candidates holding a bachelor's degree and $60,060 for candidates holding a master's degree. Salary will be commensurate with qualifications and experience.
Minimum Number of References Required
1
Maximum Number of References Allowed
2
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