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What is a from home computational biology postdoc?

A From Home Computational Biology Postdoc is a postdoctoral research position that focuses on computational biology and allows the researcher to work remotely, often from their own home. This role typically involves analyzing biological data using computational methods, developing algorithms or models to answer biological questions, and collaborating with other scientists virtually. The position requires expertise in both biology and computer science, and it may involve publishing research papers, writing code, and participating in online meetings with research teams. Remote postdoc roles have become more common as technology has enabled effective collaboration and access to large datasets from anywhere.

What are the key skills and qualifications needed to thrive as a from home computational biology postdoc?

To thrive as a From Home Computational Biology Postdoc, you need a doctoral degree in computational biology or a related field, with strong expertise in bioinformatics, statistics, and genomics. Proficiency in programming languages such as Python or R, experience with bioinformatics software, and familiarity with version control systems like Git are typically required. Excellent problem-solving abilities, independent time management, and strong written communication skills set top candidates apart. These skills enable high-quality research, effective remote collaboration, and the ability to contribute meaningful scientific insights from a virtual environment.

What are the unique challenges and benefits of working remotely as a computational biology postdoc?

Working from home as a Computational Biology Postdoc offers flexibility and the ability to focus on research without typical on-site distractions. However, it can also present challenges such as limited face-to-face collaboration with lab members, potential difficulties accessing certain computational resources, and the need to be proactive in communicating with supervisors and collaborators. Successful remote postdocs often develop strong self-management skills and make deliberate efforts to stay engaged with their research community through regular virtual meetings and online seminars. The role is well-suited for independent researchers who are comfortable leveraging digital tools for data analysis and collaboration.

What cities are hiring for From Home Computational Biology Postdoc jobs?

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What are the most commonly searched types of Computational Biology Postdoc jobs?

The most popular types of Computational Biology Postdoc jobs are:

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From Home Computational Biology Postdoc jobs near you

Postdoctoral AI Researcher in AI/ML for Cellular and Protein Computational Biology

Harvard University

Cambridge, MA • On-site

Full-time

Re-posted 19 days ago

Harvard University rating

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Job description

Position
Details
Title
Postdoctoral AI Researcher in AI/ML for Cellular and Protein Computational Biology
School
Faculty of Arts and Sciences
Department/Area
Kempner Institute at Harvard University
Position Description
The Kempner Institute at Harvard University seeks early-career researchers to help shape the future of AI as Postdoctoral AI Researchers. We are looking for candidates with deep expertise in modern AI/ML and a strong record of research accomplishment who are excited to develop new AI approaches for high-impact problems in cellular and protein computational biology.
This role focuses on applying modern AI/ML methods to protein and cellular biology, including protein structure prediction, protein-protein and small-molecule-protein docking, cell state prediction from large-scale Perturb-seq datasets, and multimodal modeling of protein function and cellular state.
We seek candidates with strong technical preparation in modern AI/ML, a demonstrated record of scholarly achievement, and experience in computational biology or biological data analysis. Strong candidates may come from protein-focused, cell-state-focused, or multimodal biological modeling backgrounds and will have expertise in one or more of the following areas:
  • foundation model training, evaluation, and adaptation
  • protein structure modeling and docking
  • cell state prediction from large-scale perturbation datasets
  • multimodal modeling for protein function and cellular state prediction
  • familiarity with AlphaFold, RFDiffusion, CellCap, or related models for protein and cellular biology

Postdoctoral AI Researchers will work closely with Kempner faculty, researchers, and students on foundational machine learning and biologically informed scientific applications. The position is particularly well-suited to candidates eager to apply their technical expertise in modern AI to important problems in protein biology, cellular systems, and biological intelligence, while continuing to grow as scholars within a collaborative academic environment.
Candidates should be within 2 years of receiving their doctoral degree and will work under the direction of Kempner Institute faculty.
Appointment Terms
  • Postdoctoral AI Researchers conduct research under the general supervision of one or more Harvard faculty members.
  • The appointment is for one year; reappointment may be possible for up to a total of three years, contingent on funding, project needs, satisfactory performance, and mutual interest.
  • This is a full-time, benefits-eligible postdoctoral appointment based at the Kempner Institute at Harvard University.
  • Due to the importance of in-person mentoring and collaboration, this position is based on campus, full-time, at Harvard University. Remote work for this position is not possible.

Basic Qualifications
  • PhD in computer science, statistics, electrical engineering, applied mathematics, computational biology, bioengineering, biophysics, or a related quantitative field required by the expected start date.
  • Candidates must have received their PhD on or after September 15, 2024, or be on track to complete all PhD requirements by the expected start date of October 15, 2026.
  • Demonstrated expertise in modern AI/ML, including deep learning and hands-on experience with frameworks such as PyTorch or JAX.
  • Strong publication record in leading venues such as ICML, ICLR, NeurIPS, RECOMB, ISMB, or comparable conferences and journals, 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 biology, biological data analysis, protein modeling, cellular modeling, or related areas.
  • 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 protein structure modeling, protein-protein docking, or small-molecule-protein docking.
  • Experience with cell state modeling from large-scale perturbation datasets, including Perturb-seq or related data.
  • Experience with multimodal modeling for protein function or cellular state prediction.
  • Familiarity with models and methods relevant to protein and cellular biology, including AlphaFold, RFDiffusion, CellCap, or related systems.
  • Experience with large-scale datasets, distributed training, or high-performance computing environments.
  • Expertise in scientific applications of AI/ML in protein biology, cellular systems, and related areas of computational biology.

Special Instructions
Please submit the following items in PDF format no later than 11:59pm EST Monday, June 8, 2026:
  • CV
  • A research statement of no more than 2 pages describing your experience using modern AI/ML for protein structure, cellular state, or related biological modeling problems. Please be specific about your individual contributions.
  • References - 2-3 required
    • Please give the emails of up to 3 individuals who can describe your previous related work.
    • 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.

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 October 15, 2026.
Contact Information
Molly Marshall
Contact Email
KempnerInstitute@Harvard.edu
Salary Range
Expected salary is $100,000, subject to compliance with the applicable salary requirements for the appointment. This is a benefits eligible position.
Minimum Number of References Required
2
Maximum Number of References Allowed
3
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