• Evaluate system architecture and design robustness: Assess whether the architecture is fit for purpose, where integration across subsystems introduces unresolved constraints, and whether single points of failure remain unaddressed. Distinguish engineering execution problems from fundamental architectural limitations. Assess technology readiness using the framework appropriate to each domain and jurisdiction.
• Assess technology readiness using the framework appropriate to each domain and jurisdiction: NASA/DoD TRL 1 to 9, European Commission TRL, ISRO and DRDO TRL with DGQA qualification, and China's CTRL for hardware and space systems; FDA IND/CTA, EMA CTA, CDSCO, PMDA, MFDS, and NMPA frameworks for biotechnology and therapeutics; IEC 61215, IEC 62133, UL 1973, ESA TRL, and grid compliance standards for energy and storage systems; FIPS 140-3, Common Criteria, and NIST post-quantum standards for cryptographic and secure-computing hardware; CE marking, BIS/IS certification, and CDSCO MDR for medical devices and infrastructure materials; DGCA RPAS, IEC 61508, and MoD qualification pathways for robotics, drones, and dual-use systems inter alia.
• Analyse subsystem co-design constraints across the seven sectors: For semiconductors and edge compute, evaluate SWAP-C tradeoffs, inference latency, quantisation feasibility, process node requirements, IP licensing dependencies (ARM, RISC-V, third-party IP), and EDA tool stack maturity. For space, defence and aerospace, evaluate payload integration, radiation hardening, secure communications stacks, and qualification against MoD and DRDO requirements. For AI, quantum and frontier computing, evaluate compute and memory bandwidth ceilings, secure enclave isolation, side-channel attack resistance, and cryptographic agility for the post-quantum transition. For energy transition and power systems, evaluate firmware security, grid protocol integration, cell-level thermal and chemistry constraints, and power electronics reliability under field conditions. For biotechnology and advanced therapeutics, evaluate process scalability from bench to GMP, assay reproducibility, and regulatory pathway timing. For robotics, automation and advanced manufacturing, evaluate sensor suite integration, perception-stack reliability, functional safety compliance, and field-robotics certification including DGCA RPAS where applicable. For advanced materials and critical minerals, evaluate process yield, scale-up risk from pilot to commercial production, input purity dependencies, and lifecycle performance under operating conditions.

• Evaluate whether patent claims cover the technically significant aspects of the innovation or merely peripheral implementation details, and whether the underlying technical disclosure is sufficient to support the claims made.
• Identify dominant IP holders and their approximate technical claims landscape within a domain, providing the context within which defensibility is assessed, without conducting formal legal FTO analysis.
• Identify the specific engineering complexity, proprietary process, or integration capability that constitutes genuine competitive moat, distinguishing it from generic implementation of known techniques.
• Assess sector-specific sources of durable advantage beyond IP, recognising that the locus of moat varies sharply across the seven domains. For semiconductors, process and packaging know-how, foundry relationships, and silicon IP licensing position. For space, defence and aerospace, qualification heritage, flight history, and clearance-dependent customer access. For AI, quantum and frontier computing, training data assets, compute access, model architectures, and cryptographic certification. For energy transition and power systems, cell chemistry know-how, manufacturing yield, and grid certification timelines. For biotechnology, clinical data, regulatory approvals, biological assets, and manufacturing process IP. For robotics and advanced manufacturing, integration know-how, customer deployment data, and functional safety certification. For advanced materials and critical minerals, process IP, feedstock access, and lifecycle and performance data accumulated under operating conditions.
• Evaluate the time and capital required for a well-funded competitor to replicate the position, recognising that in critical and emerging technologies the most durable moats often arise not from patents alone but from the combined cost of replicating capital infrastructure, qualified personnel, regulatory clearances, and accumulated process learning.

• Technical diligence memoranda in investment-grade format covering system architecture, engineering risk, subsystem co-design constraints, and unresolved challenges.
• Domain-appropriate technology readiness and risk assessment briefs, calibrated to the regulatory and certification framework relevant to each sector and jurisdiction.
• IP and competitive moat assessments spanning patents, process know-how, capital and time barriers, data and biological assets, and regulatory positioning.
• Systems architecture and integration breakdowns for priority investments across the firm's seven technology domains.
• Manufacturing and supply-chain dependency characterisations to be handed off to the Supply Chain Fellow for industrial feasibility assessment.

• Undergraduate or graduate training in electrical, mechanical, aerospace, biomedical, biochemical, chemical, or agricultural engineering; materials science; semiconductor systems; computer science; or applied physics.
• Ability to interpret complex engineering documentation, including architecture diagrams, datasheets, simulation outputs, process flow diagrams, and academic technical papers, across multiple domains.
• Familiarity with subsystem co-design constraints across at least two of the firm's seven technology domains, with the analytical disposition to extend that literacy into adjacent sectors over time.
• Understanding of IP fundamentals sufficient to assess the technical substance of patent claims and identify dominant IP holders in a domain, and an appreciation that durable competitive advantage in critical and emerging technologies often arises from sources beyond patents alone, including process know-how, capital and time barriers, data and biological assets, and regulatory clearances.
• Working literacy across domain-appropriate regulatory and readiness frameworks, including NASA/DoD and European Commission TRL, ISRO and DRDO TRL with DGQA qualification, FDA, EMA, and CDSCO clinical pathways for biotechnology, FIPS 140-3 and Common Criteria for cryptographic hardware, IEC standards and grid compliance for energy systems, CE marking and BIS/IS certification for devices and materials, and DGCA RPAS pathways for unmanned systems.
• Strong structured analytical thinking, with the ability to separate first-principles engineering assessment from marketing claims.
• Precision, rigour, and professional discretion in handling sensitive technical information.

Purus Research invests in early stage Indian deep tech startups and, drawing on the same thematic intelligence, in listed equity markets in India and globally. Our focus is on frontier technologies that shape strategic industries: space and launch systems, semiconductors and advanced computing, defence and dual use systems, biotechnology and life sciences, advanced materials and critical minerals, and clean energy and climate technologies, with quantum technologies, next generation communications, and advanced manufacturing as cross cutting domains.

The most valuable opportunities in deep tech are enabled by researchers in university and government laboratories, veterans of corporate research divisions building independently, engineers in stealth mode startups, and serial founders identifying strategic industry shifts before they become publicly visible. Finding these opportunities requires a structured intelligence function, and that is what this fellowship is cogitated to deliver.

Deep tech refers to technologies rooted in substantial scientific discovery or meaningful engineering innovation, often combining advances across multiple disciplines, aiming to solve fundamental, hard problems of significant scope. Unlike shallow tech (software, apps, business model innovation), deep tech is grounded in breakthrough science or hard engineering, addressing problems that incremental approaches cannot reach.

The consequence of building on breakthrough science or hard engineering is structural. These technologies demand long R&D cycles, often a decade or more, and heavy upfront capital before commercial viability. Their primary risk is technical, not commercial. When they succeed, they produce structurally tenable companies, protected by engineering complexity, proprietary process, and intellectual property, that reconfigure entire industries.

Deep tech investing is fundamentally an intelligence problem. The technology is complex, the signals are early, and the commercial implications require domain specific judgement. This fellowship is designed to aid Purus solve that problem systematically.

The fellowship is organised around five interconnected roles that together form an integrated intelligence system, spanning the identification of early stage technology signals, technical and industrial assessment, and the synthesis of those signals into investment decisions across private and public markets.

Each fellow's work feeds directly into the others. A technology surfaced by the Scouting Fellow is assessed by the Technical Fellow, evaluated for manufacturability, in India, by the Supply Chain Fellow, classified for strategic relevance by the Dual Use Fellow, and interpreted for listed equity implications by the Markets Convergence Fellow. The outputs are structured, decision grade, and used directly in Purus's investment process.

Informed undergraduate and graduate students with strong technical, analytical, or policy foundations, and the curiosity to apply them to aid real investment decisions. The work is research heavy and operates across professional intelligence platforms, technical documentation, and primary literature, requiring the technical literacy to navigate these tools and the analytical discipline to extract decision grade insight from them. You do not need prior finance experience. You do need intellectual rigour, comfort with ambiguity, willingness to learn, technical fluency across multiple research environments, and the discipline to produce structured, accurate work under deadlines.