Science Career Networking Opportunities

Don't go it alone - collaborate to deliver global impact with your research! Delighted to share findings from our newly published pilot-scale study on CO₂ capture heat integration. It's exciting not only because of new approach to reducing the reboiler duty by 6% and cooling duty by 24%, resulting in operating cost savings of CO2 capture. It's exciting because it proves that collaboration is essential for credible, impactful research. Our team brought together multi-institutional expertise, industrial partners, and real-world site access on a coal-fired power plant. This work was possible because this collaboration enabled: - Access to infrastructure - Operating a mobile pilot on a live power plant requires partnerships beyond any single lab. - Data rigour - Validating marginal energy gains demanded cross-disciplinary expertise, including thermodynamics, advanced data reconciliation, and process engineering. - Industrial validation - Co-developing with site operators built credibility and practical insight from day one. - Diverse expertise - Chemistry + engineering + simulation + field operations. Individual researchers miss insights that teams can easily identify. The lesson: Impact = great ideas + rigorous execution + real-world validation. Collaboration is how you deliver all three. If you're pursuing energy research with genuine traction, treat collaboration as a core strategy, not optional. Build networks early. Your best work will come from teams you haven't yet assembled. #science #research #scientist #researcher #professor #phd #CCUS #engineering

Some places remind you that collaboration isn’t just a value — it’s an engine for understanding our planet. Last week, I traveled to Sodankylä in Finnish Lapland for the launch of the European Space Agency - ESA–Finnish Meteorological Institute - Ilmatieteen laitos Arctic‑Boreal Earth Science Supersite — a world‑class hub where science, technology, and international partnership converge. Standing at the Arctic Space Centre, surrounded by decades of atmospheric, cryosphere, and ecosystem observations, you feel the weight of what global cooperation can achieve. The Sodankylä Supersite brings together European Space Agency - ESA, NASA - National Aeronautics and Space Administration, EUMETSAT, and research partners across Europe, North America, and Asia to develop and validate satellite data products essential for monitoring our climate. Its integrated observation system captures the interactions between the Earth’s surface, biosphere, and atmosphere, supported by reference instruments for missions such as NASA OCO‑2, ESA TROPOMI/Sentinel‑5P, ESA SMOS, and NASA SMAP. This site fills a critical gap in high‑latitude Earth system data, providing the ground truth that ensures global environment and climate monitoring satellites deliver accurate, actionable insights. What struck me most was the spirit of collaboration: Finnish researchers, European agencies, private‑sector innovators, and international partners working side by side. The Supersite isn’t just infrastructure — it’s a shared commitment to understanding a rapidly changing Arctic and, ultimately, a rapidly changing world. In an era where climate challenges cross borders, so must our solutions. And places like Sodankylä show what becomes possible when we build together. More information about the Supersite: https://lnkd.in/dw9gKxGJ

💡 Why networking is important for research projects and consortia? 💊 In our 10-year research project (now in year 6) and multi-country consortia (9 partners), we have a work package - Networking, which I am co-leading. The aim is to create an environment that enables knowledge sharing and fosters long-standing research partnerships. Our Networking initiative is two-fold: - Networking within the Network: focused on enhancing inter-institutional communication, organizing joint activities, and fostering knowledge sharing and partnerships among our consortium. - Networking outside the Network: collaborating with local and global research initiatives, experts, and communities to create visibility, reputation, and connectivity. Our activities include topic-specific symposia at the forefront conferences in the field; "coffee clubs" for junior-senior scientist exchange; writing of joint (expert) opinions and statements; joint funding applications and of course social gatherings. Often overlooked, networking activities are invaluable and deserve a spotlight. Honestly, I am grateful to the funding body for recognizing its significance as a separate work package and including the milestones to report on. Here's why: 🤝 Networking enables interdisciplinary approaches, breaking silos and enriching our research endeavors. 🔍 Networking provides a platform for researchers to exchange information, share insights, support each other, and stay updated on the latest developments in their respective fields. 🗺 Engaging with researchers worldwide broadens our horizons, fosters diversity in thinking, and elevates the global impact of our research. There are countless more benefits! Share your experiences and examples. P.S. We also have a Policy work package 😉 #Research #Networking #Collaboration #Innovation

🔬 Biostatistics & Epidemiology: A Powerful Partnership in Public Health 🧠📊 Epidemiology and biostatistics are not just allied disciplines—they’re inseparable pillars of modern public health. Their synergy fuels scientific discovery, guides policy, and shapes interventions that save lives. 🎯 Complementary Roles 🔍 Epidemiology: The Question Generator Epidemiologists identify public health problems, generate hypotheses, and design studies to explore disease patterns and risk factors. • Study Design: Choosing between cohort, case-control, or cross-sectional designs. • Hypothesis Generation: Spotting unusual disease clusters and exploring environmental or genetic links. 📈 Biostatistics: The Analytical Engine Biostatisticians bring rigor and structure to analysis, transforming raw data into actionable insights. • Data Analysis: Regression models, survival analysis, hypothesis testing. • Bias Control: Using stratification, matching, and advanced techniques to ensure validity. ⸻ 🔄 Collaboration Across the Research Cycle From idea to impact, these fields work hand-in-hand: • Design: Epidemiologists define the framework; biostatisticians handle power calculations and randomization. • Data Collection: Field implementation meets data integrity. • Analysis & Interpretation: Quantitative models meet contextual insight. ⸻ 🌍 Landmark Public Health Successes • Framingham Heart Study — Identified key cardiovascular risk factors. • Polio Vaccine Trials (1954) — Pioneered RCTs and statistical validation. • COVID-19 Response — Modeling and analytics shaped global strategies. ⸻ 🔬 Modern Integration & Innovation Today’s research integrates both fields in exciting ways: • Causal Inference — From associations to causation using tools like mediation analysis and instrumental variables. • Big Data & Machine Learning — Leveraging EMRs and AI for scalable insights. • Precision Medicine & Spatial Epidemiology — Mapping risk at individual and population levels. ⸻ 🚧 Challenges & 🚀 Opportunities Challenges: • Managing high-dimensional data. • Bridging training gaps between disciplines. Opportunities: • Interdisciplinary education. • Harnessing AI to merge epidemiological context with statistical power. ⸻ 🧠 At the intersection of inquiry and evidence lies the true strength of public health. As biostatistics and epidemiology evolve together, they continue to shape a healthier, data-driven world. 🌐 #PublicHealth #Biostatistics #Epidemiology #Research #DataScience #CausalInference #HealthPolicy #PrecisionMedicine #AIinHealthcare #LinkedInScience

As Harvard faces deep research funding cuts, a private equity firm has stepped in with a $39M commitment to support a Harvard research lab. Could this signal a new future for how academic science is funded? The investment comes from Turkish firm İş Private Equity, which typically backs high-growth small and medium-sized enterprises (SMEs). The funding recipient is the lab of Professor Gökhan Hotamışlıgil at the Harvard T.H. Chan School of Public Health, whose research aims to develop therapies for obesity and other metabolic diseases. Broader context Private equity (PE) rarely funds basic university research directly, as it doesn’t align with traditional return-focused models. But that’s changing. New structures are emerging where PE capital supports translational or applied academic science: ▫️ New startup - İş Private Equity launched Enlila, a new biotech company created to fund Hotamışlıgil’s lab over the next 10 years. Enlila will also invest in translating the lab’s discoveries into therapeutic products. ▫️ Joint ventures - Since 2017, Deerfield Management has created university partnerships to advance early-stage therapeutics, providing capital and helping universities evaluate projects toward Investigational New Drug (IND) readiness. Recent examples include: - Hyde Park Discovery with University of Chicago ($130M, 2025) - VeritaScience with Washington University in St. Louis ($130M, 2024) ▫️ Royalty monetization - In 2023, Purdue Research Foundation received over $100M from Blue Owl Capital by selling a portion of its royalty interest in Pluvicto, a prostate cancer therapy. Yale University executed a similar deal for the drug Yervoy, turning future royalties into immediate research capital. Takeaway As the research funding landscape evolves, the capital stack for science is becoming increasingly complex. I think we’ll likely see more private equity, venture capital, and philanthropy stepping in to support bold, high-risk science in new and unexpected ways. Curious to hear your thoughts: Should private equity be stepping into early-stage science? Which research areas could benefit most from this approach?

🔖 Interested in academia-industry collaboration in #edtech? Read this new Commentary by a group of academic researchers collaborating with EdTech companies around the world. While we’ve seen many positive examples, the work hasn’t always been easy. We’ve faced challenges such as: 🔻 Early contract terminations or pressure to reanalyse data when results didn’t show positive impacts expected by the companies 🔻Unequal legal support, with individual researchers at academic institutions lacking the resources available to large corporate legal teams 🔻 Difficulty maintaining fair project timelines, due to ongoing “mission creep” (repeated requests for new revisions beyond what was originally agreed) We advocate for: 🔺 Formalized data-sharing protocols that promote transparency and open science 🔺 Dedicated legal support units for public-private partnerships at universities 🔺A centralized, anonymized data repository to enable more rigorous cross-study analyses. This would strengthen the evidence base not just for individual companies, but for the EdTech field as a whole ✍ Article co-authored with Todd Cherner Adam Dubé Adrian Pasquarella Nicola Pitchford Dr Helen Ross 🙏Thank you Sonia Livingstone Candice Odgers and Amy Orben for starting this important debate and thank you Prof Bernadka Dubicka Editor in Chief Child and Adolescent Mental Health, for facilitating the conversation in the journal! Download from:

Academic research published during my 10+ years at Duke Institute for Health Innovation was cited over 1,000 times in 2025. Some unspoken lessons about interdisciplinary research: 1) rejection is the norm - My first first-author paper was rejected 7 times before getting accepted (https://lnkd.in/eDENrp9M). Across all publications, I would guess each publication is rejected on average 3+ times. I've only ever had one publication accepted without revisions (https://lnkd.in/eYyQfirF). 2) credit is infinitely divisible - Eagerly collaborate and include students, trainees, clinicians, and administrators in your work. Value and elevate the work that takes place to implement innovations. Two notable examples from our own work: publishing the implementation of the Sepsis Watch model with 27 collaborators (https://lnkd.in/eBXKPyGq); publishing a review on eliminating the digital divide with 50 collaborators from Health AI Partnership (https://lnkd.in/e5Bjkbiv) 3) learn the publishing norms from different disciplines - Stats / CS publish full-length manuscripts at conferences. Law review and social science articles are typically one, two, or three authors. Challenge publishing norms where you can and respect norms when you must. Be willing to provide extensive feedback on written outputs to collaborators in different disciplines, even if you can't be a named co-author. 4) peer review is mostly random - If we get critical reviews and a rejection, I typically resubmit the paper as-is to a new publication outlet. The chances two different sets of peer reviewers have the same feedback is exceedingly low. Focus effort on revisions when you have a path to publication with the same reviewers. 5) submit to new journals - MLHC, FAccT, Nature Digital Medicine, and PLOS Digital Health were all started in the last ~15 years. Don't be afraid to support new journals that cut across disciplines, especially if the journal aligns with your interdisciplinary research and interests. That journal could be big some day! 6) leverage invitations to publish - If you are invited to write a piece or edit a collection, leverage the opportunity! In 2020 we wrote an invited review that's now been cited 200+ times (https://lnkd.in/eqv_B7jt) and in 2023 we curated a collection of manuscripts that's now been viewed ~70,000 times (https://lnkd.in/e2EHdB8a). 7) invest time building skills - I spent 4 years working on healthcare data science / ML projects before participating in my first publication (https://lnkd.in/e-pENwA8). Productive != Publications. Productive can mean building skills that enable you to conduct groundbreaking research in the future.

Thrilled to share our new research published today in PNAS with brilliant colleagues Renli Wu (Northwestern University) and Christopher Esposito (UCLA Anderson)! 🎉 "Shifting power asymmetries in scientific teams reveal China's rising leadership in global science" We analyzed millions of scientific papers to understand how leadership in international collaborations is shifting by assessing who plays leadership roles in science. Here's what we found: 📊 The Leadership Shift In US-China collaborations: Chinese scientists went from leading 30% of projects (2010) to 45% (2023). Our models project parity by 2027-2028. 🔬 Critical Technologies: China is on track to reach leadership parity with the US in 8 of 11 critical technology areas before 2030—including AI, semiconductors, and advanced communications. 🌍 The Decoupling Paradox: Counterintuitively, our models show that US-China scientific decoupling would actually increase China's global scientific leadership, as Chinese scientists redirect partnerships to countries where they're more likely to lead. 🎓 Belt & Road Investments: China invested $4.6B in training international students (2012-2025), with growing focus on developing countries. This is already translating into scientific leadership in those regions. 💡 What This Means: This isn't a zero-sum game. The real question is whether competition will drive positive investments in global scientific capacity—or whether efforts to restrain progress will harm both nations and slow global advancement. The dynamics of US-China scientific competition can go in very different directions depending on policy choices made today. Paper: https://lnkd.in/g_GYzPrt Grateful to the NSF National Network for Critical Technology Evaluation and UChicago Knowledge Lab #Science #Research #Innovation #GlobalScience #SciencePolicy #China #ChinaPolicy

In an era of rising geopolitical tension, should Karolinska Institutet still collaborate with China? Writing this from China, where I’m visiting universities in Hong Kong, Shanghai and Shandong. Given today’s geopolitical climate, it’s reasonable to ask: why do we collaborate with Chinese universities? With some time to reflect, I came up with five interconnected reasons: • Patients first: Diseases ignore borders, and to offer the best prevention, diagnostics and treatments—for patients in Sweden, in low- and middle-income countries (LMICs), and globally—we must work with the strongest partners, wherever they are. • Global challenges require global science: Cancer, pandemics and antibiotic resistance demand large datasets, diverse populations and complementary expertise. Ca 10% of KI’s 7000 annual publications involve collaboration with Chinese researchers or institutions, which shows how central these partnerships already are. • World-leading environments: China hosts world-leading universities backed by massive investments in data, technology and infrastructure. In responsible partnerships, these resources can benefit patients and knowledge development far beyond China. • Bottom-up collaboration: Most collaborations start researcher-to-researcher, not government-to-government. KI researchers work with colleagues in almost every part of the world. This is how ideas grow, methods spread and young scientists are trained. • Responsible, broad global engagement: We take geopolitics, ethics and security concerns seriously—but withdrawing from collaboration will not make the world safer or healthier. Instead, we build strong frameworks for academic integrity, ethics and values, and we engage within those frameworks as part of a broad global mission that also includes close partnerships in LMICs, where health needs are greatest. So, to me, the answer is clear: yes, we should collaborate wherever it best advances knowledge and health—while standing firm on our values and responsibilities. #ResponsibleInternationalisation KI Alumni #StockholmTrio Sveriges Ambassad i Peking Consulate General of Sweden in Shanghai Business Sweden Svenska institutet Shandong University The Hong Kong University of Science and Technology The Chinese University of Hong Kong The University of Hong Kong Fudan University Shanghai Jiao Tong University Ruijin Hospital Affiliated to Shanghai Jiao Tong University, School of Medicine STINT, The Swedish Foundation for International Cooperation in Research and Higher Education Vinnova Vetenskapsrådet / Swedish Research Council Utrikespolitiska institutet AstraZeneca Berthold Jäck #QuantumImaging

The Strategic Power of AI Partnerships Several friends have pointed out that I’ve carved out a niche in AI partnerships—and they’ve asked me to explain what that actually means. It’s a space with no single definition across industry or academia, so I’ve taken a stab at outlining what’s emerging and why it matters. You'll see this space heat up and many organizations forming their own AI partnerships team. You may disagree, and that’s welcome. Defining AI Partnerships Based on my research, AI partnerships are collaborative relationships between organizations that combine complementary strengths to develop, deploy, or enhance AI solutions. These partnerships can take many forms: - Big companies joining forces to build the AI stack - Startups using cloud platforms to launch AI applications - Governments investing in public infrastructure - Research labs working with industry to commercialize innovation This builds on what Satya Nadella, Microsoft and others have identified as three distinct evolutionary phases: Bespoke Phase – Custom alliances between firms for mutual gain. Data remains siloed. Platform Democratization Phase – Wider access to AI tools and infrastructure for developers and end users. Agent Ecosystem Phase – Rich, dynamic collaborations modeled on multi-agent systems and natural ecosystems. Industry Perspectives Definitions vary depending on who you ask. IBM emphasizes access—AI partnerships as a way to democratize tools and skills. PwC sees AI partnerships as a necessity in a complex global ecosystem. MIT Sloan breaks them down structurally into: - Bilateral collaborations - AI-driven ecosystems - Vendor relationships - Research consortia - Data-sharing networks Then you have groups like the Partnership on AI, a nonprofit consortium focused on ethical frameworks. Strategic Purposes From my research, five core strategic drivers keep coming up: Resource Access – Data, talent, and compute that one party alone lacks. Cost Sharing – AI infrastructure is expensive. Collaboration spreads the load. Market Reach – Especially in regulated sectors, partnerships help crack new verticals. Reputation & Risk – Ethics, transparency, and compliance are best handled with allies. Acceleration – Shared expertise speeds up innovation without everyone reinventing the wheel. The Strategic Advantage Very few organizations can build AI alone. These systems require layered competencies—data, algorithms, infra, domain knowledge—and partnerships are how firms plug their gaps. Cloud players are partnering with startups. Car companies are teaming with AI labs. Pharma firms are linking up with research universities. These aren’t side projects—they’re core strategies. Looking Ahead The winners in AI won’t just be the ones with the most powerful models. They’ll be the ones with the most powerful networks—the best ecosystem of partners. That’s the strategic edge. More details: https://lnkd.in/g8JRsE-G