Assistant Professor (AI/ML for Biology)

About Us

Atria University desires and enables research impact beyond publications. We operate without traditional departments (HoDs). Faculty are housed within Centers of Excellence (CoE), fostering deep, cross-disciplinary collaboration. This role will primarily be affiliated with the Bio-AI Hub/CoE.

Why this role

Help build India's next wave of Bio-AI: genomic and protein foundation models, multi-omics modelling, generative design for enzymes and pathways, and AI-assisted DBTL loops with wet-lab partners. You'll have real datasets, compute, and translational collaborations.

What you'll do

• Lead research on Bio-AI foundation models (e.g., DNA FMs, protein LMs, generative design/diffusion for sequences/structures).
• Ship artifacts: open-source code, trained weights, datasets, and benchmarking pipelines; submit to top venues; file IP where appropriate.
• Collaborate intensively with faculty in the wet-lab/clinical CoEs to design unified, problem-driven interdisciplinary curricula and research projects (strain/enzyme design, microbiome, diagnostics, materials for bioenergy, etc.).
• Teach light, high-impact: 23 project-based, 4-credit sprints/year; mentor student teams on real problems.
• Win grants & lead consortia: craft proposals, coordinate multi-partner projects, and grow the Bio-AI Hub, explicitly ensuring student research teams are integrated into grant deliverables.

What will set you up for success (must-have)

• PhD (or ABD close to defense) in CS/AI/Computational Biology/Bioinformatics/Applied Math or related.
• Strong first-author record or open-source impact in sequence modelling (Transformers/GraphNNs/Diffusion) applied to genomics/proteomics.
• Hands-on with PyTorch, training/evaluating large models, and reproducible ML (MLOps, containers, Slurm/cloud).
• Exposure to workings of foundation models

Nice to have

• Experience with at least one: genomic FMs (e.g., Enformer-style, Nucleotide/Genome LMs), protein LMs (e.g., ESM/ProtT5/MSA), or similar
• Enzyme/pathway design, multi-omics integration, metagenomics.
• Joint work with wet-lab/clinical teams; familiarity with DBTL or LIMS/ELN.
• Prior grant success (PI/Co-PI) or industry collaboration.

What we offer

• Research-first load: concentrated teaching in short sprints; significant time for research.
• Compute & infra: access to GPUs, curated omics datasets, secure data rooms, and DevOps support.
• Translational runway: partnerships with industry and research organizations; pathways for IP and spinouts.
• Community: interdisciplinary peers in AI and Life Sciences; vibrant Bengaluru ecosystem.