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Weekend Computer Science Environmental Jobs in Boston, MA

... environment and the ability to develop new approaches to complex design problems Experience with developing production grade algorithms for the web scale Preferred - Ph.D. in Computer Science ...

... environment and the ability to develop new approaches to complex design problems Experience with developing production grade algorithms for the web scale Preferred - Ph.D. in Computer Science ...

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Weekend Computer Science Environmental information

What is the difference between Weekend Computer Science Environmental vs Weekend Data Analyst?

AspectWeekend Computer Science EnvironmentalWeekend Data Analyst
Required CredentialsRelevant computer science or environmental certifications, degreesData analysis certifications, degrees in statistics or related fields
Work EnvironmentOutdoor or lab settings for environmental work; computer labs for CSOffice or remote settings, analyzing data on computers
Employer & Industry UsageEnvironmental agencies, tech companies with environmental projectsBusinesses, research firms, government agencies analyzing data
Common Search & ComparisonOften compared for weekend or part-time roles involving tech or environmentCompared for data analysis roles, but less so with environmental focus

The comparison between Weekend Computer Science Environmental and Weekend Data Analyst highlights differences mainly in work environment and certifications. While both roles involve computer skills, the environmental role emphasizes outdoor or lab work, whereas data analysts focus on office-based data processing. Understanding these distinctions helps job seekers choose roles aligned with their skills and interests.

What are the most commonly searched types of Computer Science Environmental jobs in Boston, MA? The most popular types of Computer Science Environmental jobs in Boston, MA are:
Postdoctoral Fellowship in Electrical and Computer Engineering

Postdoctoral Fellowship in Electrical and Computer Engineering

Harvard University

Cambridge, MA • On-site

$67K - $91K/yr

Full-time

Re-posted 22 days ago


Harvard University rating

8.4

Company rating: 8.4 out of 10

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Job description

Position
Details
Title
Postdoctoral Fellowship in Electrical and Computer Engineering
School
Harvard John A. Paulson School of Engineering and Applied Sciences
Department/Area
Computer Science
Position Description
The John A. Paulson School of Engineering and Applied Sciences (SEAS) and the Department of Statistics at Harvard University seeks applicants for a postdoctoral fellow in Electrical and Computer Engineering.
This position is for a Postdoctoral Scholar in the area of information theory and artificial intelligence at the Harvard Information Theory Laboratory The successful candidate will work under the supervision of Prof. Flavio Calmon at Harvard SEAS. The postdoctoral researcher will develop information-theoretic methods for alignment, privacy, and reliability in modern AI systems.
Basic Qualifications
Applicants must have a PhD in Computer Science, Electrical Engineering, Applied Mathematics, or a related discipline, or be confident of its completion by the start of this position.
Additional Qualifications
Successful candidates will have publications in information theory and machine learning venues, such as IEEE Transactions on Information Theory, ISIT, NeurIPS, ICML, ICLR, and ACM FAccT. Experience in machine learning and information theory, and expertise in at least one of the following areas is preferred: AI alignment, (differential) privacy, and coding or information theory for AI systems. Proficiency in Python and experience with GPU cluster environments (e.g., SLURM) are a plus.
Special Instructions
Please provide a CV, a Research Statement, and two or more letters of recommendation.
The target start date is September 2026 (flexible). The position is funded for two years.
Contact Information
Sarah Gayer
Contact Email
sgayer@seas.harvard.edu
Salary Range
$67,600 - $91,826
Pay offered to the selected candidate is dependent on factors such as rank, years of experience, training or qualification, field of scholarship, and accomplishments in the field
Minimum Number of References Required
2
Maximum Number of References Allowed
5
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