How to Find PhD Supervisors in Bioinformatics and Computational Biology (and Get Funded)

"Bioinformatics" and "computational biology" rarely name a single department you can search the way you'd search a chemistry or math faculty list. Most of the time they name a track that runs across computer science, statistics, biology, and a medical school at once, stitched together through a formal interdepartmental program rather than one hallway. That changes how you search for a supervisor, which committee reviews your application, and which mechanism, NIGMS, NLM, or NSF, actually pays your stipend. This guide covers what's specific to this field: the interdepartmental program model, the training-grant mechanisms that exist only here, and how to read a computational lab's real capacity when its "equipment" is a compute cluster rather than a microscope.

The field runs on interdepartmental programs, not single departments

UCLA's Bioinformatics Interdepartmental Program (IDP) is the clearest real example of how this gets built. It runs an integrated one-year core curriculum shared by every incoming student, more than 50 approved electives, and lab rotations, with faculty mentors drawn from more than 12 separate departments across the university. UCLA's own program description is direct about why it's structured this way: quantitative and biomedical training programs have very different cultures and structures, particularly at the doctoral level, and the IDP exists specifically to combine the strengths of both rather than force students into one or the other. Its rotation system is modeled on biomedical PhD programs rather than a typical computer science PhD, because, as the program states, students typically don't yet know whose lab they want to join at admission.

That pattern repeats elsewhere under different names. UNC Charlotte's Bioinformatics and Computational Biology PhD requires two first-year research rotations and expects incoming competence across biochemistry, cell and molecular biology, genetics, statistics, and computer science at once, a genuinely wide prerequisite spread compared with a single-discipline PhD. Indiana's bioinformatics PhD at IUPUI structures the degree around core courses, rotations, a required minor in an outside area, qualifying exams, and a dissertation committee that has to represent more than one discipline. UCSF frames its program directly as training scientific leaders at the interface between computation, statistics, and biology, rather than placing it inside any one of those three departments. Boston University's PhD in Bioinformatics requires three laboratory rotation units and an oral qualifying exam before candidacy, run through its own named program rather than borrowed wholesale from CS or biology.

Practically, your search works two ways at once: identify the specific professor whose research fits what you want to do, as in our genetics and genomics guide, since an IDP model treats faculty affiliation as secondary to the actual research fit, but also check how that program runs committees, since a dissertation committee spanning three departments, common here, means your thesis committee is drawn from a wider, less predictable pool than in a single-department PhD.

NIGMS has a training-grant category built specifically for this field

Beyond the generic NIH biomedical T32 that funds predoctoral training broadly, NIGMS maintains a named predoctoral training-grant program area called Computational Biology, Bioinformatics, and Biomedical Data Science, listed alongside other named areas like Genetics and Cellular, Biochemical, and Molecular Sciences on NIGMS's own predoctoral training grant program page. This is a real, field-specific funding category rather than a generic label applied after the fact. The University of Delaware holds an active T32 in exactly this area, grant number T32-GM142603, which recruits six trainees annually from eligible second-year PhD students with a stipend and full tuition coverage. UNC's Bioinformatics and Computational Biology program has had its own NIGMS T32 renewed for a further five years, supporting five BCB students during their second year of graduate training. As with any NIGMS T32, these grants fund institutions, which build the training program and select trainees, not individual applicants directly, and slots carry the standard NRSA rule: the trainee must be a US citizen, non-citizen national, or lawful permanent resident at the time of appointment.

Searching under this exact program-area name is a more precise question than asking generically whether "NIH funds this department," since a program can hold a T32 under this category even if its department name contains neither "bioinformatics" nor "computational."

The NLM T15 is a mechanism this field has that others don't

This is the detail most specific to bioinformatics and computational biology: the National Library of Medicine, NLM, a distinct NIH institute from NIGMS or NHGRI, runs its own named T15 training grant specifically for biomedical informatics and data science, a category this site's other field hubs don't cover because it doesn't apply to them. Johns Hopkins describes its T15 eligibility requirement as being a citizen or a noncitizen national of the United States, or having been lawfully admitted for permanent residence at the time of appointment, and Vanderbilt's page states the identical rule, the same standard NRSA citizenship requirement that applies to NIH T32 and F31 funding generally, with no special exception for international applicants despite being a mechanism unique to this field. An older NLM institutional solicitation went further and stated plainly that individuals on temporary or student visas are not eligible, language current institutional pages don't repeat verbatim but whose underlying citizen-or-permanent-resident rule still holds. Utah's T15 page notes that its postdoctoral positions specifically are limited to US citizens or green card holders, narrower than its own predoctoral rule, worth knowing if you're looking at a postdoc rather than a PhD slot. NLM itself says to contact T15-awarded institutions directly for trainee selection, eligibility, and support questions, since specifics are set institutionally rather than by one blanket NLM rule.

Dry lab and wet lab funding mostly runs on the same scale, with one named exception

A reasonable assumption going in is that a "dry lab" computational PhD position might be funded differently from a "wet lab" one, since it carries no bench consumables or reagent costs. The evidence for an actual stipend gap is thin and mostly anecdotal: most institutions pay a single, flat departmental or program-wide stipend regardless of whether a given student's project is computational or bench-based. What is real and worth knowing by name is the Damon Runyon Quantitative Biology Fellowship, which explicitly funds people from quantitative backgrounds moving into computational cancer biology under joint mentorship from a computational scientist and a wet-lab cancer biologist at once, an institutionalized version of exactly the dry-lab-plus-wet-lab pairing this field is built around. The caveat matters: it's postdoctoral, not something a PhD applicant can apply for, and it requires the research be based at a US institution. Treat it as evidence that dual dry-lab and wet-lab mentorship is a recognized, fundable model here, useful when you're evaluating whether a prospective PI's co-mentorship structure is a known pattern, rather than as funding you can use as a current PhD applicant.

Compute and cluster access is this field's version of equipment access

This site's biochemistry guide treats cryo-EM core access as a real signal of lab capacity worth asking about directly. Computational biology has a direct equivalent: a PI's access to compute infrastructure, not a microscope but a cluster, genuinely varies between institutions and even between labs at the same institution, and it's worth asking about rather than assuming. Shared cores exist specifically to solve this. UNC Greensboro's BSBC lab was built because compute-intensive algorithms often become bottlenecks for many labs, and it allocates HPC time on a merit basis across users rather than let any one lab hoard capacity. UConn's Computational Biology Core has described offering free, unlimited HPC access to students, postdocs, and faculty through its shared cluster, worth comparing against a target institution's own policy. Not every institution gets this right: a bioinformatics group at NC State named a lack of computational equipment as a major impediment, and specifically noted that general campus clusters are often configured for conventional academic computing and can be hard to customize for bioinformatics workloads, a different failure mode than simply having too little hardware. A University of Pittsburgh library report separately flagged limited remote HPC access as a constraint for off-site researchers. None of this means a lab without dedicated hardware is under-resourced, most computational biology genuinely runs on shared infrastructure by design. It does mean that if a professor's project depends on heavy computation, asking directly what cluster or cloud-compute access their group has, and whether it's shared, allocated, or effectively unlimited, is a legitimate question before you commit your application focus, the same way you'd ask a wet-lab PI about core-facility access.

Checking who's active: bioRxiv's q-bio section, software releases, and the field's own conferences

PubMed and Google Scholar still work here, but two things are specific to this field. First, bioRxiv runs dedicated quantitative biology subject collections, including Bioinformatics and Genomics among others, and because a large share of current computational work posts as a preprint well before formal peer review, checking a professor's recent q-bio preprints is a more current signal than their indexed publication list alone. Second, distinct from most wet-lab fields, a meaningful share of this field's output is software, not a wet-bench result: tool papers published in journals like Bioinformatics, Genome Biology, and Nucleic Acids Research, usually paired with a public GitHub or GitLab repository. Checking whether a lab's tools have recent commits, answered issues, and active releases is a useful activity signal with no real equivalent in a field where the output is bench data, not maintained code. Beyond publications, ISCB, the International Society for Computational Biology, is the field's main professional body and runs the ISMB conference along with regional RECOMB and ECCB meetings where current work gets presented before journal publication. ISCB also runs student travel fellowships for these conferences, open to US, Canadian, and other international applicants, but these are conference travel awards, not PhD funding.

A practical search order

• Search a target university under both "bioinformatics" and "computational biology," and check whether the program runs as a formal interdepartmental or interdisciplinary program, since faculty mentors are frequently drawn from more departments than the program's own name suggests.
• Check whether a target program holds an NIGMS T32 specifically under the Computational Biology, Bioinformatics, and Biomedical Data Science program area, a more precise question than asking generically whether NIH funds the department.
• If you're a US citizen, non-citizen national, or permanent resident, check whether the program holds an NLM T15 grant, a mechanism specific to this field, and confirm current eligibility directly with the institution rather than an older solicitation.
• If a target professor's work is computationally heavy, ask directly what compute or cluster access their group has and whether it's shared, allocated, or effectively unlimited, the computational equivalent of asking a wet-lab PI about core-facility access.
• Cross-check a professor's activity on bioRxiv's quantitative biology collections and, where relevant, the commit history and releases on their lab's public code repositories, not just their indexed publication list.

Once you've worked out which professors genuinely fit and whose labs are active right now, our guide on checking a professor's grant funding before emailing them walks through the direct tools for confirming active funding, including NIH RePORTER. GradScoutFunding handles the search itself: it finds professors by field and country, confirms they're actively publishing, and drafts a personalized first email grounded in one of their real recent papers for you to review and send yourself, it never sends anything automatically, and it never claims to verify that a professor currently has funding available, only that they're currently active. The free tier gives 100 credits with no card required, and paid credit packs are one-time purchases that never expire.

Common questions