Assistant Professor (AI/ML)

About Us

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

Why this role

Help translate research into impact: translational research and applying foundational AI/ML techniques to complex, real-world challenges in areas such as Weather, Biology/Biotech, and Energy. This role is ideal for a candidate who prioritizes R&D and industry-sponsored projects over a heavy teaching load, as all teaching is designed to be project-based and high-impact.

What you'll do

• Research applying traditional and advanced ML models (e.g., XGBoost, CNNs, UNets etc) to high-impact challenges in cross-domain applications (e.g., forecasting, genomic data analysis, smart grid optimization).
• Explore and apply Foundation Models (FMs) or large language models (LLMs) to specialized, domain-specific tasks, leveraging transfer learning and fine-tuning.
• Build and ship tangible research outputs, including publishing in top-tier conferences/journals, contributing to open-source codebases, trained model weights, and curated datasets.
• Proactively pursue and secure research grants (PI/Co-PI) and industry sponsorships. Explicitly integrate student research teams into grant deliverables, directly supporting the Learning by Doing mandate.
• Collaborate closely with faculty in other Centers of Excellence (e.g., AI for Biology, Digital Energy, Circular Economy) to design and execute integrated, cross-disciplinary research projects.
• Teach light, high-impact: 23 project-based, 4-credit sprints/year; mentor student teams on real problems.

What will set you up for success (Must-Have)

• MS / Ph.D. (or ABD close to defense) in Computer Science, Artificial Intelligence, Applied Mathematics, Electrical Engineering, or a related quantitative field.
• Strong technical record and experience in classical Machine Learning algorithms (e.g., optimization, statistical learning, boosting) and Deep Learning architectures (e.g., CNNs, RNNs, Transformers).
• Demonstrated experience (via publications, projects, or industry work) applying AI/ML models in a scientific / deep-expertise domain.
• Hands-on expertise with modern ML frameworks like PyTorch or TensorFlow, and familiarity with best practices for reproducible ML (MLOps, containers, cloud/cluster computing).

Desired Attributes (Nice-to-Have)

• Experience with Foundation Models or Large Language Models (LLMs), including model adaptation, fine-tuning, or deployment exposure.
• Prior grant success (PI/Co-PI) or substantial experience collaborating with industry on funded projects.
• Experience with high-performance computing (HPC) environments or large-scale data processing.
• A strong open-source impact or a first-author publication record demonstrating applied research excellence.

What We Offer

• A deliberately research-first load with concentrated, short-sprint teaching to maximize time for R&D.
• Access to GPU-compute infrastructure, secure data rooms, and DevOps support.
• Dedicated pathways for IP filing, spinouts, and high-level partnerships with industry and research organizations to ensure real-world impact.
• An interdisciplinary peer community and affiliation with the vibrant technology and research ecosystem in Bengaluru.