Assistant Professor
6 days ago
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: 2–3 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.
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