SUMMARY:Your mandate is to identify, investigate, and de-risk platform-level opportunities in porous media - materials and mechanisms that could define entirely new product categories for H&V. You will report directly to senior R&D leadership, operating with genuine scientific autonomy. Title depends on experience and track record in the industry.
ESSENTIAL FUNCTIONS AND RESPONSIBILITIES: - Scan the porous media landscape - nonwovens, polymer membranes, MOFs, zeolites, aerogels, other fibrous structures - for convergence zones and white-space opportunities invisible from within a single material class.
- Formulate original research hypotheses, design experiments to de-risk them, and execute at TRL 1-3 with full scientific independence.
- Build and maintain an active presence in the external scientific community: publish, present, collaborate with academic partners, and serve as H&V's scientific antenna in your domain.
- Apply computational and AI-assisted tools - high-throughput experimentation, design of experiments, Bayesian optimization - to accelerate discovery cycles.
- Communicate findings clearly to senior R&D and executive leadership: what you found, what it means, and - just as importantly - what you killed and why.
- Identify opportunities to connect emerging platform concepts to H&V's existing technical and manufacturing capabilities where genuine synergy exists.
- Lead or aid in the commercialization of platform concepts
EDUCATION AND EXPERIENCE: - PhD in STEM category - Chemical, Mechanical, Material sciences, and other porous media related disciplines or BS/MS degrees with equivalent work experience
- A track record of originating research and successful commercialization,
- First-author publications in peer-reviewed journals and/or Named inventorship (not just assignee)
- 10+ years of product development experience
STRONGLY DIFFERENTIATING: - Cross-domain literacy across the porous media landscape. Deep expertise in one material class is expected;
- Computational and AI fluency: proficiency in design of experiments and Bayesian optimization (required); Python or equivalent scripting for data analysis (strongly preferred); familiarity with high-throughput experimentation platforms or closed-loop optimization systems (a real differentiator).
- An active, generative external scientific network - not passive literature consumption. Conference participation, invited presentations, peer-review activity, or academic collaborations.
- The intellectual disposition to propose ideas that contradict current product logic and defend them with data.
- Previous technology scouting experience
