Science Innovation Ecosystem

I’ve reviewed thousands of job applications from academic scientists looking to move into biotech startups. Here’s how the best applications stood out ⤵️ Sharing this for folks graduating from PhDs this year or thinking about a change - it’s still a tough market out there, but one that’s hopefully improving! _______ 1️⃣ Show how your personal values align to the company mission. Why? Startups want to change the future. Demonstrate you’ve been independently working towards that same future → this indicates you’ll work hard & find the day to day meaningful. How? Example, for a company developing phages to treat antibiotic resistant bacteria: ✅ My PhD research focused on optimising a gene therapy for children suffering from grey platelet syndrome. During that time, I volunteered in the pediatrics ward. I am motivated by improving health outcomes for the most vulnerable. ❌ Having finished my PhD, I am looking to make the jump into industry. _______ 2️⃣ Directly explain how your scientific expertise can solve the startup’s problems. Why? This shows your ability to connect the dots between “the company problem that needs to be solved” and “the impact I can have.” Startup MVPs have proactivity in spades. How? Example, for a company developing cultured meat: ✅ A big problem for the cultured meat industry is developing immortalised, scalable cell lines. As a genetic engineer, I can generate cell lines capable of feeding millions of people. ❌ My 6 years of experience with mammalian cell culture and background in genetic editing make me a great fit for your company. _______ 3️⃣ Incorporate metrics (beyond publications!) into your resume. Why? Publications = academic currency. Scientific breakthroughs allowing a company to get profitable and survive = startup currency. Publications require detailed science capable of getting past peer-review. Startups require time-boxed, outcomes-oriented science. That’s really different! Metrics indicate you already understand that shift in mindset - and no matter what your project focused on, you can frame it in terms of startup-relevant metrics. How? ✅ Supported two summer students to achieve xyz outcome in three months ✅ Generated 5 novel immune complexes in 2 months ✅ Achieved XYZ while dropping experiment costs by 20% ❌ Conducted a research project analysing how XYZ ❌ Published in a prestigious journal. _______ 4️⃣ Show - don’t state - your communication & collaboration skills. Why? These skills are 10x more important when working at a fast pace with people from different professional backgrounds. How? ✅ Three-minute thesis contest ✅ Industry/startup work experience ✅ Engagement with an entrepreneurship community ✅ Cross-discipline collaboration ✅ A well-written career summary connecting the dots between your skills & the value you can bring to the company. As always, builds or add-ons welcome: I made some of these mistakes when I first graduated from my PhD, you don’t have to 😉

EXCLUSIVE: Three young MIT researchers are launching their new biotech startup today, having raised a $28 million seed round from investors like a16z, Amplify Partners, and Zetta Venture Partners. The company, called Boltz, was formed in late 2024, partially in response to seeing AI models in biology growing more capable yet less accessible. As a public-benefit corporation, Boltz aims to keep open-sourcing its major models, while making a business in selling software and services in deploying these in the lab. “What we started seeing with AlphaFold 3 is the best models are becoming more and more closed,” CEO Gabriele Corso told me. “The level of impact of these models is how good the models are multiplied by the number of people who are able to access them. We want to maximize both sides of the equation.” More on the future of a 13-employee startup worth watching closely in Endpoints News: https://lnkd.in/eTiFrQy8

500 students share one computer in Niger. Yet they're conducting advanced physics experiments that students at elite schools can't access. The secret? WebAR turning basic smartphones into portable STEM labs. Think about that. In Sub-Saharan Africa, fewer than 10% of schools have internet. Student-to-computer ratios hit 500:1. Yet mobile subscriptions jumped from single digits to 80% in a decade. Students already carry the infrastructure—we just weren't using it right. Traditional EdTech Reality: ↳ VR headsets: $300+ per student ↳ Heavy apps requiring 5G speeds ↳ Labs costing millions to build ↳ Rural schools: permanently excluded The WebAR Revolution: ↳ Runs in any browser, optimized for 3G ↳ No app store, minimal storage ↳ Science scores improving 10-15% ↳ Every smartphone becomes a laboratory But here's what grabbed me: A physics teacher in rural South Africa has one broken oscilloscope. No budget. Her students scan printed markers, and electromagnetic fields pulse across their desks. They run experiments infinitely—no equipment damaged, no reagents consumed. One student told her: "Engineering is for people like me now. The lab fits in my pocket." What changes everything: ↳ Mobile-first matches actual connectivity ↳ Browser-based works offline ↳ Teachers need training, not new buildings ↳ Inequality becomes irrelevant The Multiplication Effect: 1 teacher with markers = 30 students experimenting 10 schools sharing content = communities transformed 100 districts adopting = educational equality emerging At scale = STEM education without infrastructure gaps We spent decades waiting for labs that won't arrive. Now any browser becomes one. Because when a student in rural Africa explores the same 3D molecules as someone at MIT—using the phone already in their pocket—you realize: WebAR isn't shiny technology. It's a quiet equaliser making world-class STEM education fit into 3G connections and $50 phones. Follow me, Dr. Martha Boeckenfeld for innovations where accessibility drives transformation. ♻️ Share if you believe quality education shouldn't require perfect infrastructure.

The ultimate book that I've been searching for years 📕   For those who know me (or follow my content), you'd know that I love learning geography, history and culture of different countries. I often procrastinate by reading random Wikipedia articles and exploring cities on Google Maps street view. I especially love maps…so much so that I even started the websites "Global AI Regulation Tracker" and "Note2Map", which are all about tracking stuff on a world map!   An angle that I'm always curious about is "tech geography". i.e. the 'soft' culture, mindsets, language, stories and habits that shape a tech hub or smart city (beyond government regulation, policies and programs). For a while, I had been looking for a book *specifically* on modern geography of tech hubs around the world (not just Silicon Valley).   Indeed, it's a niche ask. I didn't expect there would be any book of this kind, and that it would be on me to read widely and connect the dots myself.   But finally, I stumbled upon this newly published book in my local bookstore. True to its title, *The New Geography of Innovation (2025)* by Mehran Gul dives into the 'secret sauce' behind the tech hubs of US, China, UK, South Korea, Singapore, Switzerland, Germany and Canada.   When I say 'secret sauce', I'm talking about really specific and fascinating fact nuggets, such as: 💡 The history of Tsinghua University and the origin of China's tech talent 💡 How minor kinks in California's labour laws shaped Silicon Valley 💡 The 3 cycles of Korean tech entrepreneurship 💡 How tech talent runs deep in Singapore government 💡 How Swiss university fee structure and trains led to a tech boom 💡 How evolving family-business culture will shape the future of German tech   Gul provides balanced narratives, backed up with countless case studies and authentic expert interviews, all tied together in an engaging fun-to-read story.   This is not a sponsored post btw. I genuinely cherish this book, and actually read it twice (first for leisure, second for notetaking). It's a book that I'll regularly refer back to whenever I study a new country. It's one book worth 10 books. Highly recommend it. #book #tech #read #digital #recommend

Make deforestation newsworthy. That’s a core principle behind much of the work we do at Mongabay. We're not as eye-catching as Greenpeace (pictured), but we doggedly report on it. I highlighted this as one of our key strategies during a talk on Friday at the International Society of Tropical Foresters (ISTF) conference hosted at the Yale School of the Environment. My talk covered 5 main themes, including the role of science communication in tropical forest governance. One of the biggest gaps in forest governance is communication. Policy change is nearly impossible if the right information doesn’t reach the right people at the right time. And let’s be honest—many of the conversations happening in expert circles aren’t resonating with the decision-makers and communities who matter most. Good communication isn’t a footnote in conservation. It’s fundamental to progress. Here are 7 ways science communication can strengthen forest governance: Raise awareness & drive engagement ↳ Well-communicated science makes deforestation, degradation, and ecosystem services more accessible. 🌳 e.g. Forests don’t just store carbon—they regulate the water cycle. Water security is tangible to people in ways CO₂ isn’t. Making that connection can shift priorities. Broaden the constituency for forests ↳ Messaging tailored to local contexts builds public demand for better governance. 🔥 e.g. Environmental crises are becoming personal for more people. When science is clear and credible, it expands the base of those who care. Shape policy & promote transparency ↳ Translating scientific data into actionable insights helps leaders make informed decisions. 📉 e.g. Satellite imagery in the Amazon helped drive policies that contributed to a sharp drop in deforestation in Brazil. Foster cross-sector collaboration ↳ Effective communication aligns scientists, policymakers, businesses, and NGOs toward shared goals. 🐘 e.g. Emerging research links biodiversity loss to reduced carbon storage in forests—bringing two historically separate fields together. Build trust & navigate complexities ↳ Accessible, transparent communication increases public buy-in for science-based policies. 🤝 e.g. People are more likely to support solutions when they understand the science behind them. Facilitate behavioral change ↳ Science can influence consumer and corporate decisions by showing the real-world impact of unsustainable practices. 🌴 e.g. Data on deforestation for palm oil fueled campaigns that led to corporate zero-deforestation commitments—and a significant decline in forest clearing for the crop in Indonesia 🇮🇩. Inspire new ideas & innovation ↳ Stories of success empower people. Solutions can give them something to act on. 🌈 e.g. “Bad news drains me. Solutions make me feel like I can do something.” This shift in framing fuels creativity and action. The takeaway? If we want better forest governance, we need better science communication.

𝗪𝗵𝘆 𝗱𝗼 𝘀𝗼𝗺𝗲 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘁𝗲𝗮𝗺𝘀 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝘄𝗵𝗶𝗹𝗲 𝗼𝘁𝗵𝗲𝗿𝘀 𝘀𝘁𝗮𝗹𝗹—𝗱𝗲𝘀𝗽𝗶𝘁𝗲 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗷𝘂𝘀𝘁 𝗮𝘀 𝗵𝗮𝗿𝗱? In research conducted with Johnathan Cromwell, Kevin J. Johnson, and Amy Edmondson, we studied more than 160 innovation teams—including those in a Fortune Global 500 company—and found that it's not just how much teams learn that matters, but when and how they learn. We identified four core modes of team learning: 𝗥𝗲𝗳𝗹𝗲𝘅𝗶𝘃𝗲 — assessing goals, roles, and strategies 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝗹 — brainstorming, prototyping, testing new ideas 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 — scanning the environment for trends, signals, and shifts 𝗩𝗶𝗰𝗮𝗿𝗶𝗼𝘂𝘀 — drawing lessons from others who’ve done similar work The most effective teams didn’t try to do everything at once. They began and ended with reflexive learning, anchoring their work in shared understanding. They placed exploratory learning (experimental and contextual) in the middle. This rhythm—reflection → exploration → reflection—helped them reduce friction, integrate insights, and build real momentum. We also found that vicarious learning can be combined with reflexive learning in the same project phase with positive results. But when teams mixed reflexive with experimental or contextual learning in the same phase, performance suffered. 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Innovation doesn’t thrive on more learning. It thrives on structured learning. Teams that sequence and separate their learning activities make faster, clearer progress. We’ve summarized the findings from our research, published in Administrative Science Quarterly—a leading journal in organizational research—in this new Harvard Business Review article. Link in comments.

How do people around the world feel about breakthrough science? For me this is one of the key questions of my day-to-day work at Bayer. Let me share a few thoughts why. In light of escalating threats like climate change, food insecurity, and strained health systems, the demand for innovation is greater than ever. At the same time, scientific progress has always been a prerequisite for prosperous companies and, consequently, for stable societies. Innovations should therefore be welcomed with open arms. Unfortunately, I often experience the opposite: While scientific advances are accelerating, disinformation and ideological blinders fuel fear and resistance to potential breakthroughs. But it does no good to just lament this; we need to engage with people argumentatively where they are. Against this background I am more than happy that my colleagues at Leaps by Bayer shared today the Breakthrough Study —a landmark piece of research conducted in collaboration with Boston Consulting Group (BCG) and market research by Ipsos UK. Over 13,000 participants in 13 countries shared what they know and think about AI in healthcare, cell and gene therapies, cultivated meat, and new genomic techniques in agriculture, with the key objective to empower industry leaders, policymakers, investors, and other stakeholders to take a closer look at the people we hope to serve through innovation. I was deeply impressed by the depth of insights gathered by Leaps by Bayer, BCG, and Ipsos. One key takeaway? In many countries, public enthusiasm for innovation outpaces policy. For policymakers, this is a wake-up call: we must bridge the gap between public sentiment and regulatory action to unlock the full potential of science. Additionally, the study reinforced the importance of education. The data shows a clear correlation between knowledge and optimism - those who know more about innovation are more positive about it. it. The Study also found high levels of neutrality and uncertainty—particularly concerning cultivated meat and NGTs. Addressing target groups with mainly neutral views on new technologies could prove a critical focus to foster greater openness. Let's use this finding as a proof point to further strengthen education efforts and help translate complex scientific concepts into accessible communication. 

Today, India boasts 4,000+ DeepTech startups, all of them with significant momentum in AI, semiconductors, quantum computing, aerospace, robotics, advanced manufacturing, health-tech, and clean energy. But deep-tech startups in India have long confronted a bitter paradox: daring ideas and bold intent but policy support only once they survived. The main issue is 🔺 long gestation — DeepTech products in semiconductors, aerospace, quantum, robotics, or advanced materials will typically take 5–10 years for R&D to get into the market and the capital markets want faster returns. This results in an enduring gap in patient capital and scarce domestic funds to maintain ongoing investment through long technology validation phases. 🔺 Another great difficulty is high capital intensity. Hardware labs, fabrication access, testing infrastructure, and certifications are high on the steep hill of development cost, and early stage survival without consistent policy/institutional support can be challenging. 🔺 𝐓𝐡𝐞 𝐦𝐚𝐧𝐝𝐚𝐭𝐨𝐫𝐲 𝐭𝐡𝐫𝐞𝐞-𝐲𝐞𝐚𝐫 𝐰𝐚𝐢𝐭 𝐟𝐨𝐫 𝐃𝐒𝐈𝐑 (𝐃𝐞𝐩𝐚𝐫𝐭𝐦𝐞𝐧𝐭 𝐨𝐟 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐟𝐢𝐜 𝐚𝐧𝐝 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡) 𝐫𝐞𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧 𝐨𝐟𝐭𝐞𝐧 𝐩𝐮𝐬𝐡𝐞𝐝 𝐛𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 “𝐯𝐚𝐥𝐥𝐞𝐲 𝐨𝐟 𝐝𝐞𝐚𝐭𝐡” 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐜𝐨𝐧𝐜𝐞𝐩𝐭 𝐚𝐧𝐝 𝐜𝐨𝐦𝐦𝐞𝐫𝐜𝐢𝐚𝐥 𝐯𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲. That barrier has now been decisively removed. By eliminating the three-year existence requirement for DSIR recognition, the Union Government has made a quiet but consequential correction. It signals confidence in India’s young innovators and a shift toward evaluating startups on technological merit and scientific readiness not age. 𝐅𝐨𝐫 𝐞𝐚𝐫𝐥𝐲-𝐬𝐭𝐚𝐠𝐞 𝐝𝐞𝐞𝐩-𝐭𝐞𝐜𝐡 𝐯𝐞𝐧𝐭𝐮𝐫𝐞𝐬, 𝐭𝐡𝐢𝐬 𝐢𝐬 𝐚 𝐠𝐞𝐧𝐮𝐢𝐧𝐞 𝐠𝐚𝐦𝐞 𝐜𝐡𝐚𝐧𝐠𝐞𝐫: 𝐚𝐜𝐜𝐞𝐬𝐬 𝐭𝐨 𝐜𝐫𝐞𝐝𝐢𝐛𝐢𝐥𝐢𝐭𝐲, 𝐢𝐧𝐬𝐭𝐢𝐭𝐮𝐭𝐢𝐨𝐧𝐚𝐥 𝐛𝐚𝐜𝐤𝐢𝐧𝐠 𝐚𝐧𝐝 𝐟𝐮𝐧𝐝𝐢𝐧𝐠 𝐩𝐚𝐭𝐡𝐰𝐚𝐲𝐬 𝐰𝐡𝐞𝐧 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐬𝐭, 𝐧𝐨𝐭 𝐲𝐞𝐚𝐫𝐬 𝐥𝐚𝐭𝐞𝐫. India’s own experience validates this approach. #ideaForge, #Niramai, #signalchip, #EtherealX, #Agnikul, #MolbioDiagnostics all demonstrate how early belief and timely institutional support can convert lab-stage innovation into national capability whether in defence, space or healthcare. On the ground, many startups failed not because ideas were weak, but because validation came too late. Whether it was affordable cancer diagnostics, climate-resilient agriculture, or indigenous energy storage, innovators needed support in year one not year four. By aligning DSIR recognition with scientific validation cycles, supported by initiatives like the new DSIR deep-tech guidelines, PRISM Network Platform, and Creative India 2025, India is clearly moving from survival-based screening to capability-based empowerment.

Most startup hubs are built around talent. Cambridge is built around breakthroughs. It’s one of the few ecosystems in Europe where: → Deep tech is rooted in foundational science → Founders often hold patents before pitch decks → Researchers are fluent in both academia and commercialization → Lab discovery and market application are done in the same building Top innovative startups in the scene: Jagex – Gaming giant behind RuneScape, acquired multiple times Luminance – AI-powered legal document analysis and compliance platform IQGeo – SaaS solution for geospatial network management and digital twins Nuclera – Pioneering rapid desktop DNA and protein synthesis Nyobolt – High-power, ultra-fast charging battery technology Lightcast – AI-driven single-cell analysis for biotech research Fetch.ai – Decentralized AI platform for autonomous machine learning Healx – AI-driven drug discovery for rare diseases Sano Genetics – Personalized genomics research with patient-driven data clock.bio – Extending human healthspan through longevity biotech Levidian – Decarbonization tech using graphene and hydrogen innovation Five AI – Autonomous vehicle software company acquired by Bosch Gearset – DevOps platform for Salesforce CI/CD and automation Uncommon – Cultivated meat company tackling sustainable food production Quantinuum – Quantum computing powerhouse formed from Honeywell’s spin-off Risilience – AI-powered climate risk modeling for enterprises Qureight – AI-driven medical data analysis for precision medicine Secondmind – AI-powered decision-making for automotive applications Nu Quantum – Quantum networking and encryption solutions Biofidelity – Ultra-sensitive molecular diagnostics for early cancer detection Epitopea – Next-gen cancer immunotherapy using novel antigens Darktrace – AI-powered cybersecurity unicorn, acquired in 2023 Forefront RF – Next-gen RF chips for 5G and mobile communications Abselion – Advancing biosensor tech for molecular diagnostics Wave Photonics – Quantum photonics innovation for optical computing CorrosionRADAR – AI-based predictive maintenance for industrial assets Charco – Wearable tech to improve Parkinson’s symptoms Humanloop – AI model optimization for natural language applications Immaterial – Nanotech-based gas storage and material solutions Riverlane – Quantum computing software company optimizing qubit performance Cambridge is an ecosystem where science scales. Did we miss any cool tech startups? 𝐖𝐡𝐨 𝐢𝐬 i5invest: We are a corporate development firm with access to 150K+ top decision-makers in Strategy, Business Development, and M&A. We provide innovative tech founders with insights, expertise, and access to our network to take their companies to the next level. #growth #tech #strategy #startups #artificialintelligence Source: Dealroom.co

Last month, a team of scientists and physicians achieved something extraordinary: they developed and delivered the first-ever personalized #CRISPR therapy to treat an infant with a life-threatening #raredisease — in just six months. A one-letter change in the baby’s DNA was corrected using a custom-built gene editor. The child, who was once facing the prospect of a liver transplant, is now steadily improving. It’s a powerful example of what’s becoming possible at the intersection of #science and #technology, urgency and purposeful ambition. And this isn’t an isolated win. Across labs, clinics, and companies, CRISPR is being used as a therapeutic modality to correct inherited disorders, engineer immune cells, disable viral DNA, and even edit entire chromosomes. New gene-editing systems—like TIGR-Tas, unveiled earlier this year—are expanding what’s possible in tissues or conditions where current tools fall short. Clinical results are emerging fast—and the pace of #innovation is only picking up. At Recursion, we’re also applying #geneediting tools like CRISPR beyond therapeutics—using the technology as a tool to better understand #biology at scale. By systematically “knocking out” thousands of individual genes and measuring how those changes affect cell behavior, we’re generating large, structured datasets that feed directly into #AI models. This is helping us uncover new biological relationships and power #drugdiscovery in ways that were previously unimaginable. What ties all of this together is a commitment to applying game-changing #innovation in service of real, urgent human needs. It signals a much-needed mindset shift in #healthcare and #biopharma: to move faster, think bigger, and tackle challenges once considered out of reach—and to truly deliver on the promise of #precisionmedicine. And we’re seeing this ambition in many other areas as well – just last week, for example, GRAIL announced more promising than ever performance stats for its #Galleri blood test for the early detection of 50+ types of #cancer. There’s still work ahead to ensure breakthroughs translate into broad, equitable impact. But this moment – this momentum – is worth pausing to recognize. We’re no longer just imagining a future where science works smarter and faster for patients. We’re building it.